93 citations · 313 across the 24 of their papers we have counts for
41 papers
Learning and Retrieval from Prior Data for Skill-based Imitation Learning
Soroush Nasiriany, Tian Gao, Ajay Mandlekar +1
Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requireme…
Visually Grounded Task and Motion Planning for Mobile Manipulation
Xiaohan Zhang, Yifeng Zhu, Yan Ding +3
Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve…
Ditto: Building Digital Twins of Articulated Objects from Interaction
Zhenyu Jiang, Cheng-Chun Hsu, Yuke Zhu
Digitizing physical objects into the virtual world has the potential to unlock new research and applications in embodied AI and mixed reality. This work focuses on recreating inter…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Josiah Wong, Viktor Makoviychuk, Anima Anandkumar +1
Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through int…
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong +7
Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch…