1 citations · 1 across the 6 of their papers we have counts for
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DexFuture: Hierarchical Future-State Visuomotor Targeting for Bimanual Dexterous Tool Use
Runfa Blark Li, Kuang-Ting Tu, Nikola Raicevic +6
Bimanual dexterous tool use remains challenging for robots due to high-dimensional hand configurations and complex hand-tool-object dynamics and contact. Most existing control poli…
Rainbow-DemoRL: Combining Improvements in Demonstration-Augmented Reinforcement Learning
Dwait Bhatt, Shih-Chieh Chou, Nikolay Atanasov
Several approaches have been proposed to improve the sample efficiency of online reinforcement learning (RL) by leveraging demonstrations collected offline. The offline data can be…
PhysGraph: Physically-Grounded Graph-Transformer Policies for Bimanual Dexterous Hand-Tool-Object Manipulation
Runfa Blark Li, David Kim, Xinshuang Liu +7
Bimanual dexterous manipulation for tool use remains a formidable challenge in robotics due to the high-dimensional state space and complicated contact dynamics. Existing methods n…
Seeing the Bigger Picture: 3D Latent Mapping for Mobile Manipulation Policy Learning
Sunghwan Kim, Woojeh Chung, Zhirui Dai +5
In this paper, we demonstrate that mobile manipulation policies utilizing a 3D latent map achieve stronger spatial and temporal reasoning than policies relying solely on images. We…
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment
Tianyu Wang, Dwait Bhatt, Xiaolong Wang +1
This paper focuses on transferring control policies between robot manipulators with different morphology. While reinforcement learning (RL) methods have shown successful results in…