IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality – RSS 2023
Tags: Deep reinforcement learning, Learning from demonstrations
Algorithms, systems, and tools that solve assembly tasks in simulation with reinforcement learning (RL) and successfully achieve policy transfer to a real-world robotic assembly system.
Square grid trajectory flight on a Crazyflie2.1. with a 1 hidden layer 4 neuron neural controller
Tags: Deep reinforcement learning, Meta learning
A tiny (single hidden layer 4 neuron) neural network, estimated by HyperPPO, driving a quadcopter to track a square grid trajectory.
Bezier Curve trajectory flight on a Crazyflie2.1. with a 1 hidden layer 4 neuron neural controller
Tags: Deep reinforcement learning, Meta learning
A tiny (single hidden layer 4 neuron) neural network, estimated by HyperPPO, driving a quadcopter to track a 3D random Bezier curve.
Efficiently Learning Small Policies for Locomotion and Manipulation
Tags: Deep reinforcement learning, Meta learning
Leveraging graph hyper networks to learn graph hyper policies trained with off-policy reinforcement learning.
Efficient Multi-Task Learning via Iterated Single-Task Transfer
Tags: Deep reinforcement learning, Meta learning
Multi-task Reinforcement Learning (MTRL) for robots to acquire new skills.
Decentralized Control of Quadrotor Swarms with End to end Deep Reinforcement Learning (Simulation)
Tags: Deep reinforcement learning, Learning from demonstrations
Decentralized Control of Quadrotor Swarms with End to end Deep Reinforcement Learning (Physical)
Tags: Deep reinforcement learning, Learning from demonstrations
Learning Equality Constraints for Motion Planning on Manifolds
Tags: Deep reinforcement learning, Motion planning
Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN). The idea behind it is to learn a level-set function of the constraint by aligning subspaces in the network with sub-spaces of the data such that it can be integrated into a constrained sampling-based motion planner. We combine both learned constraints and analytically described constraints into the planner and use a projection-based strategy to find valid points. We evaluate ECoMaNN on its representation capabilities of constraint manifolds, the impact of its individual loss terms, and the motions produced with it.
Related Publications
Sutanto, Giovanni; Rayas Fernández, Isabel M.; Englert, Peter; Ramachandran, Ragesh K.; Sukhatme, Gaurav S.: Learning Equality Constraints for Motion Planning on Manifolds. CoRR, vol. abs/2009.11852, 2020[PDF] [Video]
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Tags: Deep reinforcement learning, Learning from demonstrations, Meta learning
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in a simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts’ state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing low-level quadrotor controller without the use of a stabilizing PD controller; as well as the first work that analyses the transfer capability of a single policy to multiple quadrotors.
Publications
GranularGym: High Performance Simulation for Robotic Tasks with Granular Materials.
Millard, David; Pastor, Daniel; Bowkett, Joseph; Backes, Paul; Sukhatme, Gaurav S.: GranularGym: High Performance Simulation for Robotic Tasks with Granular Materials. Bekris, Kostas E.; Hauser, Kris; Herbert, Sylvia L.; Yu, Jingjin (Ed.): Robotics: Science and Systems XIX, Daegu, Republic of Korea, July 10-14, 2023, 2023
IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality.
Tang, Bingjie; Lin, Michael A.; Akinola, Iretiayo; Handa, Ankur; Sukhatme, Gaurav S.; Ramos, Fabio; Fox, Dieter; Narang, Yashraj S.: IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality. Bekris, Kostas E.; Hauser, Kris; Herbert, Sylvia L.; Yu, Jingjin (Ed.): Robotics: Science and Systems XIX, Daegu, Republic of Korea, July 10-14, 2023, 2023
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Language-Informed Transfer Learning for Embodied Household Activities.
Jiang, Yuqian; Gao, Qiaozi; Thattai, Govind; Sukhatme, Gaurav S.: Language-Informed Transfer Learning for Embodied Household Activities. CoRR, vol. abs/2301.05318, 2023
Alexa Arena: A User-Centric Interactive Platform for Embodied AI.
Gao, Qiaozi; Thattai, Govind; Gao, Xiaofeng; Shakiah, Suhaila; Pansare, Shreyas; Sharma, Vasu; Sukhatme, Gaurav S.; Shi, Hangjie; Yang, Bofei; Zheng, Desheng; Hu, Lucy; Arumugam, Karthika; Hu, Shui; Wen, Matthew; Guthy, Dinakar; Chung, Cadence; Khanna, Rohan; Ipek, Osman; Ball, Leslie; Bland, Kate; Rocker, Heather; Rao, Yadunandana; Johnston, Michael; Ghanadan, Reza; Mandal, Arindam; Hakkani-Tþr, Dilek; Natarajan, Prem: Alexa Arena: A User-Centric Interactive Platform for Embodied AI. CoRR, vol. abs/2303.01586, 2023
Learned Parameter Selection for Robotic Information Gathering.
Denniston, Christopher E.; Salhotra, Gautam; Kangaslahti, Akseli; Caron, David A.; Sukhatme, Gaurav S.: Learned Parameter Selection for Robotic Information Gathering. CoRR, vol. abs/2303.05022, 2023
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning.
Batra, Sumeet; Tjanaka, Bryon; Fontaine, Matthew C.; Petrenko, Aleksei; Nikolaidis, Stefanos; Sukhatme, Gaurav S.: Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning. Submitted to Neurips 2023, 2023
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Generating Behaviorally Diverse Policies with Latent Diffusion Models.
Hegde, Shashank; Batra, Sumeet; Zentner, K. R.; Sukhatme, Gaurav S.: Generating Behaviorally Diverse Policies with Latent Diffusion Models. Submitted to Neurips 2023, 2023
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QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control.
Huang, Zhehui; Batra, Sumeet; Chen, Tao; Krupani, Rahul; Kumar, Tushar; Molchanov, Artem; Petrenko, Aleksei; Preiss, James A.; Yang, Zhaojing; Sukhatme, Gaurav S.: QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control. CoRR, vol. abs/2306.09537, 2023
A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search.
Trabucco, Brandon; Sigurdsson, Gunnar A.; Piramuthu, Robinson; Sukhatme, Gaurav S.; Salakhutdinov, Ruslan: A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search. The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023
Tracking Fast Trajectories with a Deformable Object using a Learned Model.
Preiss, James A.; Millard, David; Yao, Tao; Sukhatme, Gaurav S.: Tracking Fast Trajectories with a Deformable Object using a Learned Model. 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022, pp. 1351-1357, IEEE, 2022
Sensing the Sensor: Estimating Camera Properties with Minimal Information.
Ghosh, Pradipta; Liu, Xiaochen; Qiu, Hang; Vieira, Marcos A. M.; Sukhatme, Gaurav S.; Govindan, Ramesh: Sensing the Sensor: Estimating Camera Properties with Minimal Information. ACM Trans. Sens. Networks, vol. 18, no. 2, pp. 28:1-28:26, 2022
SSL Enables Learning from Sparse Rewards in Image-Goal Navigation.
Majumdar, Arjun; Sigurdsson, Gunnar A.; Piramuthu, Robinson; Thomason, Jesse; Batra, Dhruv; Sukhatme, Gaurav S.: SSL Enables Learning from Sparse Rewards in Image-Goal Navigation. Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; SzepesvÆri, Csaba; Niu, Gang; Sabato, Sivan (Ed.): International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, pp. 14774-14785, PMLR, 2022
Privacy Preserving Visual Question Answering.
Bara, Cristian-Paul; Ping, Qing; Mathur, Abhinav; Thattai, Govind; MV, Rohith; Sukhatme, Gaurav S.: Privacy Preserving Visual Question Answering. CoRR, vol. abs/2202.07712, 2022
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning.
Sharma, Vasu; Goyal, Prasoon; Lin, Kaixiang; Thattai, Govind; Gao, Qiaozi; Sukhatme, Gaurav S.: CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning. CoRR, vol. abs/2208.13626, 2022
Efficiently Learning Small Policies for Locomotion and Manipulation.
Hegde, Shashank; Sukhatme, Gaurav S.: Efficiently Learning Small Policies for Locomotion and Manipulation. CoRR, vol. abs/2210.00140, 2022
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CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation.
Dorbala, Vishnu Sashank; Sigurdsson, Gunnar A.; Piramuthu, Robinson; Thomason, Jesse; Sukhatme, Gaurav S.: CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation. CoRR, vol. abs/2211.16649, 2022
OpenD: A Benchmark for Language-Driven Door and Drawer Opening.
Zhao, Yizhou; Gao, Qiaozi; Qiu, Liang; Thattai, Govind; Sukhatme, Gaurav S.: OpenD: A Benchmark for Language-Driven Door and Drawer Opening. CoRR, vol. abs/2212.05211, 2022
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following.
Gao, Xiaofeng; Gao, Qiaozi; Gong, Ran; Lin, Kaixiang; Thattai, Govind; Sukhatme, Gaurav S.: DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following. IEEE Robotics Autom. Lett., vol. 7, no. 4, pp. 10049-10056, 2022
Efficient Multi-Task Learning via Iterated Single-Task Transfer.
Zentner, K. R.; Puri, Ujjwal; Zhang, Yulun; Julian, Ryan; Sukhatme, Gaurav S.: Efficient Multi-Task Learning via Iterated Single-Task Transfer. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022, pp. 10141-10146, IEEE, 2022
Learning to Act with Affordance-Aware Multimodal Neural SLAM.
Jia, Zhiwei; Lin, Kaixiang; Zhao, Yizhou; Gao, Qiaozi; Thattai, Govind; Sukhatme, Gaurav S.: Learning to Act with Affordance-Aware Multimodal Neural SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022, pp. 5877-5884, IEEE, 2022
Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed.
Sutanto, Giovanni; Rombach, Katharina; Chebotar, Yevgen; Su, Zhe; Schaal, Stefan; Sukhatme, Gaurav S.; Meier, Franziska: Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed. Int. J. Robotics Res., vol. 41, no. 13-14, pp. 1121-1145, 2022
Suboptimal coverings for continuous spaces of control tasks.
Preiss, James A.; Sukhatme, Gaurav S.: Suboptimal coverings for continuous spaces of control tasks. CoRR, vol. abs/2104.11865, 2021
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning.
Zentner, K. R.; Julian, Ryan; Puri, Ujjwal; Zhang, Yulun; Sukhatme, Gaurav S.: Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning. CoRR, vol. abs/2106.13237, 2021
Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion.
Suglia, Alessandro; Gao, Qiaozi; Thomason, Jesse; Thattai, Govind; Sukhatme, Gaurav S.: Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion. CoRR, vol. abs/2108.04927, 2021
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning.
Batra, Sumeet; Huang, Zhehui; Petrenko, Aleksei; Kumar, Tushar; Molchanov, Artem; Sukhatme, Gaurav S.: Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning. CoRR, vol. abs/2109.07735, 2021
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence.
Roy, Nicholas; Posner, Ingmar; Barfoot, Tim D.; Beaudoin, Philippe; Bengio, Yoshua; Bohg, Jeannette; Brock, Oliver; Depatie, Isabelle; Fox, Dieter; Koditschek, Daniel E.; Lozano-PØrez, TomÆs; Mansinghka, Vikash; Pal, Christopher J.; Richards, Blake A.; Sadigh, Dorsa; Schaal, Stefan; Sukhatme, Gaurav S.; ThØrien, Denis; Toussaint, Marc; Panne, Michiel: From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR, vol. abs/2110.15245, 2021
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges.
Zhao, Yizhou; Lin, Kaixiang; Jia, Zhiwei; Gao, Qiaozi; Thattai, Govind; Thomason, Jesse; Sukhatme, Gaurav S.: LUMINOUS: Indoor Scene Generation for Embodied AI Challenges. CoRR, vol. abs/2111.05527, 2021
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning.
Batra, Sumeet; Huang, Zhehui; Petrenko, Aleksei; Kumar, Tushar; Molchanov, Artem; Sukhatme, Gaurav S.: Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning. Faust, Aleksandra; Hsu, David; Neumann, Gerhard (Ed.): Conference on Robot Learning, 8-11 November 2021, London, UK, pp. 576-586, PMLR, 2021
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation.
Liu, I-Chun Arthur; Uppal, Shagun; Sukhatme, Gaurav S.; Lim, Joseph J.; Englert, Peter; Lee, Youngwoon: Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation. Faust, Aleksandra; Hsu, David; Neumann, Gerhard (Ed.): Conference on Robot Learning, 8-11 November 2021, London, UK, pp. 641-650, PMLR, 2021
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NeuralSim: Augmenting Differentiable Simulators with Neural Networks.
Heiden, Eric; Millard, David; Coumans, Erwin; Sheng, Yizhou; Sukhatme, Gaurav S.: NeuralSim: Augmenting Differentiable Simulators with Neural Networks. IEEE International Conference on Robotics and Automation, ICRA 2021, Xi'an, China, May 30 - June 5, 2021, pp. 9474-9481, IEEE, 2021
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Suboptimal coverings for continuous spaces of control tasks.
Preiss, James A.; Sukhatme, Gaurav S.: Suboptimal coverings for continuous spaces of control tasks. Jadbabaie, Ali; Lygeros, John; Pappas, George J.; Parrilo, Pablo A.; Recht, Benjamin; Tomlin, Claire J.; Zeilinger, Melanie N. (Ed.): Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021, 7-8 June 2021, Virtual Event, Switzerland, pp. 547-558, PMLR, 2021
Meta Learning via Learned Loss.
Bechtle, Sarah; Molchanov, Artem; Chebotar, Yevgen; Grefenstette, Edward; Righetti, Ludovic; Sukhatme, Gaurav S.; Meier, Franziska: Meta Learning via Learned Loss. 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021, pp. 4161-4168, IEEE, 2020
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation.
Julian, Ryan; Swanson, Benjamin; Sukhatme, Gaurav S.; Levine, Sergey; Finn, Chelsea; Hausman, Karol: Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation. CoRR, vol. abs/2004.10190, 2020
Plan-Space State Embeddings for Improved Reinforcement Learning.
Pflueger, Max; Sukhatme, Gaurav S.: Plan-Space State Embeddings for Improved Reinforcement Learning. CoRR, vol. abs/2004.14567, 2020
Learning Manifolds for Sequential Motion Planning.
Rayas Fernández, Isabel M.; Sutanto, Giovanni; Englert, Peter; Ramachandran, Ragesh K.; Sukhatme, Gaurav S.: Learning Manifolds for Sequential Motion Planning. RSS 2020 Learning (in) Task and Motion Planning Workshop, 2020
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Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed.
Sutanto, Giovanni; Rombach, Katharina; Chebotar, Yevgen; Su, Zhe; Schaal, Stefan; Sukhatme, Gaurav S.; Meier, Franziska: Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed. CoRR, vol. abs/2007.00450, 2020
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap.
Heiden, Eric; Millard, David; Coumans, Erwin; Sukhatme, Gaurav S.: Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap. CoRR, vol. abs/2007.06045, 2020
Learning Equality Constraints for Motion Planning on Manifolds.
Sutanto, Giovanni; Rayas Fernández, Isabel M.; Englert, Peter; Ramachandran, Ragesh K.; Sukhatme, Gaurav S.: Learning Equality Constraints for Motion Planning on Manifolds. CoRR, vol. abs/2009.11852, 2020
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NeuralSim: Augmenting Differentiable Simulators with Neural Networks.
Heiden, Eric; Millard, David; Coumans, Erwin; Sheng, Yizhou; Sukhatme, Gaurav S.: NeuralSim: Augmenting Differentiable Simulators with Neural Networks. CoRR, vol. abs/2011.04217, 2020
Scaling simulation-to-real transfer by learning a latent space of robot skills.
Julian, Ryan; Heiden, Eric; He, Zhanpeng; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Scaling simulation-to-real transfer by learning a latent space of robot skills. Int. J. Robotics Res., vol. 39, no. 10-11, 2020
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning.
Julian, Ryan; Swanson, Benjamin; Sukhatme, Gaurav S.; Levine, Sergey; Finn, Chelsea; Hausman, Karol: Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning. Kober, Jens; Ramos, Fabio; Tomlin, Claire J. (Ed.): 4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA, pp. 2120-2136, PMLR, 2020
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments.
Yamada, Jun; Lee, Youngwoon; Salhotra, Gautam; Pertsch, Karl; Pflueger, Max; Sukhatme, Gaurav S.; Lim, Joseph J.; Englert, Peter: Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments. Kober, Jens; Ramos, Fabio; Tomlin, Claire J. (Ed.): 4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA, pp. 589-603, PMLR, 2020
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning.
Petrenko, Aleksei; Huang, Zhehui; Kumar, Tushar; Sukhatme, Gaurav S.; Koltun, Vladlen: Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp. 7652-7662, PMLR, 2020
Estimating Metric Scale Visual Odometry from Videos using 3D Convolutional Networks.
Koumis, Alexander S.; Preiss, James A.; Sukhatme, Gaurav S.: Estimating Metric Scale Visual Odometry from Videos using 3D Convolutional Networks. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019, pp. 265-272, IEEE, 2019
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors.
Molchanov, Artem; Chen, Tao; Hünig, Wolfgang; Preiss, James A.; Ayanian, Nora; Sukhatme, Gaurav S.: Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019, pp. 59-66, IEEE, 2019
Reachability and Differential based Heuristics for Solving Markov Decision Processes.
Debnath, Shoubhik; Liu, Lantao; Sukhatme, Gaurav S.: Reachability and Differential based Heuristics for Solving Markov Decision Processes. CoRR, vol. abs/1901.00921, 2019
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times.
Debnath, Shoubhik; Liu, Lantao; Sukhatme, Gaurav S.: Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times. CoRR, vol. abs/1901.01229, 2019
Accelerating Goal-Directed Reinforcement Learning by Model Characterization.
Debnath, Shoubhik; Sukhatme, Gaurav S.; Liu, Lantao: Accelerating Goal-Directed Reinforcement Learning by Model Characterization. CoRR, vol. abs/1901.01977, 2019
Interactive Differentiable Simulation
Heiden, Eric; Millard, David; Zhang, Hejia; Sukhatme, Gaurav S.: Interactive Differentiable Simulation CoRR, vol. abs/1905.10706, 2019
Meta-Learning via Learned Loss.
Chebotar, Yevgen; Molchanov, Artem; Bechtle, Sarah; Righetti, Ludovic; Meier, Franziska; Sukhatme, Gaurav S.: Meta-Learning via Learned Loss. CoRR, vol. abs/1906.05374, 2019
Rover-IRL: Inverse Reinforcement Learning With Soft Value Iteration Networks for Planetary Rover Path Planning.
Pflueger, Max; Agha-Mohammadi, Ali-Akbar; Sukhatme, Gaurav S.: Rover-IRL: Inverse Reinforcement Learning With Soft Value Iteration Networks for Planetary Rover Path Planning. IEEE Robotics Autom. Lett., vol. 4, no. 2, pp. 1387-1394, 2019
Learning to Act in Partially Structured Dynamic Environment.
Huang, Chen; Liu, Lantao; Sukhatme, Gaurav S.: Learning to Act in Partially Structured Dynamic Environment. 2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018, AAAI Press, 2018
Accelerating Goal-Directed Reinforcement Learning by Model Characterization.
Debnath, Shoubhik; Sukhatme, Gaurav S.; Liu, Lantao: Accelerating Goal-Directed Reinforcement Learning by Model Characterization. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, October 1-5, 2018, pp. 1-9, IEEE, 2018
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times.
Debnath, Shoubhik; Liu, Lantao; Sukhatme, Gaurav S.: Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, October 1-5, 2018, pp. 7063-7070, IEEE, 2018
Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT.
Abdelzaher, Tarek F.; Ayanian, Nora; Basar, Tamer; Diggavi, Suhas N.; Diesner, Jana; Ganesan, Deepak; Govindan, Ramesh; Jha, Susmit; Lepoint, TancrŁde; Marlin, Benjamin M.; Nahrstedt, Klara; Nicol, David M.; Rajkumar, Raj; Russell, Stephen; Seshia, Sanjit A.; Sha, Fei; Shenoy, Prashant J.; Srivastava, Mani B.; Sukhatme, Gaurav S.; Swami, Ananthram; Tabuada, Paulo; Towsley, Don; Vaidya, Nitin H.; Veeravalli, Venugopal V.: Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT. 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018, Vienna, Austria, July 2-6, 2018, pp. 1129-1138, IEEE Computer Society, 2018
Region Growing Curriculum Generation for Reinforcement Learning.
Molchanov, Artem; Hausman, Karol; Birchfield, Stan; Sukhatme, Gaurav S.: Region Growing Curriculum Generation for Reinforcement Learning. CoRR, vol. abs/1807.01425, 2018
Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations.
He, Zhanpeng; Julian, Ryan; Heiden, Eric; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations. CoRR, vol. abs/1810.02422, 2018
Data-driven learning and planning for environmental sampling.
Ma, Kai-Chieh; Liu, Lantao; Heidarsson, Hordur Kristinn; Sukhatme, Gaurav S.: Data-driven learning and planning for environmental sampling. J. Field Robotics, vol. 35, no. 5, pp. 643-661, 2018
Scaling Simulation-to-Real Transfer by Learning Composable Robot Skills.
Julian, Ryan; Heiden, Eric; He, Zhanpeng; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Scaling Simulation-to-Real Transfer by Learning Composable Robot Skills. Xiao, Jing; Krüger, Torsten; Khatib, Oussama (Ed.): Proceedings of the 2018 International Symposium on Experimental Robotics, ISER 2018, Buenos Aires, Argentina, November 5-8, 2018, pp. 267-279, Springer, 2018
Regrasping Using Tactile Perception and Supervised Policy Learning.
Chebotar, Yevgen; Hausman, Karol; Kroemer, Oliver; Sukhatme, Gaurav S.; Schaal, Stefan: Regrasping Using Tactile Perception and Supervised Policy Learning. 2017 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 27-29, 2017, AAAI Press, 2017
Informative planning and online learning with sparse Gaussian processes.
Ma, Kai-Chieh; Liu, Lantao; Sukhatme, Gaurav S.: Informative planning and online learning with sparse Gaussian processes. 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, May 29 - June 3, 2017, pp. 4292-4298, IEEE, 2017
Reachability and Differential Based Heuristics for Solving Markov Decision Processes.
Debnath, Shoubhik; Liu, Lantao; Sukhatme, Gaurav S.: Reachability and Differential Based Heuristics for Solving Markov Decision Processes. Amato, Nancy M.; Hager, Greg; Thomas, Shawna L.; Torres-Torriti, Miguel (Ed.): Robotics Research, The 18th International Symposium, ISRR 2017, Puerto Varas, Chile, December 11-14, 2017, pp. 387-404, Springer, 2017
Data-Driven Learning and Planning for Environmental Sampling.
Ma, Kai-Chieh; Liu, Lantao; Heidarsson, Hordur Kristinn; Sukhatme, Gaurav S.: Data-Driven Learning and Planning for Environmental Sampling. CoRR, vol. abs/1702.01848, 2017
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets.
Hausman, Karol; Chebotar, Yevgen; Schaal, Stefan; Sukhatme, Gaurav S.; Lim, Joseph J.: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. Guyon, Isabelle; Luxburg, Ulrike; Bengio, Samy; Wallach, Hanna M.; Fergus, Rob; Vishwanathan, S. V. N.; Garnett, Roman (Ed.): Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 1235-1245, 2017
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning.
Chebotar, Yevgen; Hausman, Karol; Zhang, Marvin; Sukhatme, Gaurav S.; Schaal, Stefan; Levine, Sergey: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. Precup, Doina; Teh, Yee Whye (Ed.): Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 703-711, PMLR, 2017
Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning.
Chebotar, Yevgen; Hausman, Karol; Su, Zhe; Sukhatme, Gaurav S.; Schaal, Stefan: Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016, pp. 1960-1966, IEEE, 2016
Contact localization on grasped objects using tactile sensing.
Molchanov, Artem; Kroemer, Oliver; Su, Zhe; Sukhatme, Gaurav S.: Contact localization on grasped objects using tactile sensing. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016, pp. 216-222, IEEE, 2016
Online trajectory optimization to improve object recognition.
Potthast, Christian; Sukhatme, Gaurav S.: Online trajectory optimization to improve object recognition. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016, pp. 4765-4772, IEEE, 2016
Informative Planning and Online Learning with Sparse Gaussian Processes.
Ma, Kai-Chieh; Liu, Lantao; Sukhatme, Gaurav S.: Informative Planning and Online Learning with Sparse Gaussian Processes. CoRR, vol. abs/1609.07560, 2016
Generalizing Regrasping with Supervised Policy Learning.
Chebotar, Yevgen; Hausman, Karol; Kroemer, Oliver; Sukhatme, Gaurav S.; Schaal, Stefan: Generalizing Regrasping with Supervised Policy Learning. Kulic, Dana; Nakamura, Yoshihiko; Khatib, Oussama; Venture, Gentiane (Ed.): International Symposium on Experimental Robotics, ISER 2016, Tokyo, Japan, October 3-6, 2016, pp. 622-632, Springer, 2016
Hierarchical Approaches to Estimate Energy Expenditure Using Phone-Based Accelerometers.
Vathsangam, Harshvardhan; Schroeder, E. Todd; Sukhatme, Gaurav S.: Hierarchical Approaches to Estimate Energy Expenditure Using Phone-Based Accelerometers. IEEE J. Biomed. Health Informatics, vol. 18, no. 4, pp. 1242-1252, 2014
Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles.
Hollinger, Geoffrey A.; Pereira, Arvind; Sukhatme, Gaurav S.: Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 6-10, 2013, pp. 5593-5599, IEEE, 2013
Towards practical energy expenditure estimation with mobile phones.
Vathsangam, Harshvardhan; Zhang, Mi; Tarashansky, Alexander; Sawchuk, Alexander A.; Sukhatme, Gaurav S.: Towards practical energy expenditure estimation with mobile phones. Matthews, Michael B. (Ed.): 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 3-6, 2013, pp. 74-79, IEEE, 2013
Towards a generalized regression model for on-body energy prediction from treadmill walking.
Vathsangam, Harshvardhan; Emken, B. Adar; Schroeder, E. Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S.: Towards a generalized regression model for on-body energy prediction from treadmill walking. 5th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2011, Dublin, Ireland, May 23-26, 2011, pp. 168-175, IEEE, 2011
One-step-ahead kinematic compressive sensing.
Hover, Franz S.; Hummel, Robert; Mitra, Urbashi; Sukhatme, Gaurav S.: One-step-ahead kinematic compressive sensing. Workshops Proceedings of the Global Communications Conference, GLOBECOM 2011, 5-9 December 2011, Houston, Texas, USA, pp. 1314-1319, IEEE, 2011
Networked Active Sensing of Structures.
Chintalapudi, Krishna; Caffrey, John; Govindan, Ramesh; Johnson, Erik A.; Krishnamachari, Bhaskar; Masri, Sami F.; Sukhatme, Gaurav S.: Networked Active Sensing of Structures. Prasanna, Viktor K.; Iyengar, S. Sitharama; Spirakis, Paul G.; Welsh, Matt (Ed.): Distributed Computing in Sensor Systems, First IEEE International Conference, DCOSS 2005, Marina del Rey, CA, USA, June 30 - July 1, 2005, Proceedings, pp. 387-388, Springer, 2005
Emergent Robot Differentiation for Distributed Multi-Robot Task Allocation.
Dahl, Torbjłrn S.; Mataric, Maja J.; Sukhatme, Gaurav S.: Emergent Robot Differentiation for Distributed Multi-Robot Task Allocation. Alami, Rachid; Chatila, Raja; Asama, Hajime (Ed.): Distributed Autonomous Robotic Systems 6, Proceedings of the 7th International Symposium on Distributed Autonomous Robotic Systems, DARS 2004, Toulouse, France, June 23-25, 2004, pp. 201-210, Springer, 2004
Multi-robot task-allocation through vacancy chains.
Dahl, Torbjłrn S.; Mataric, Maja J.; Sukhatme, Gaurav S.: Multi-robot task-allocation through vacancy chains. Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA 2003, September 14-19, 2003, Taipei, Taiwan, pp. 2293-2298, IEEE, 2003
