1 citations · 1 across the 14 of their papers we have counts for
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One Policy, Many Styles: Where to Aggregate Preferences for Steerable Humanoid Control?
Ziqian Huang, Yuzhuo Chen, Shimin Zhang +3
Modern robotic agents are increasingly expected to support steerable control, where a single policy can adjust its behavioral style at runtime under changing user preferences. Pref…
Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning
Varun Giridhar, Anant Khandelwal, Jeremy A. Collins +2
Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a policy that fails cannot learn from…
FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning
Chuhao Zhou, Liquan Wang, Shuxin Cao +5
While humans readily repurpose a book, a stone, or a shoe to drive a nail, robots trained on specific tools fail to transfer the same function to novel ones -- a gap we formalize a…
Hierarchical Policy Learning via Spectral Decomposition
Shuxin Cao, Liquan Wang, Walker Byrnes +3
In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By analyzing actions in the spe…
TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
Liquan Wang, Jiangjie Bian, Eric Heiden +1
Robotic manipulation tasks involving cutting deformable objects remain challenging due to complex topological behaviors, difficulties in perceiving dense object states, and the lac…
OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model
Ishika Singh, Ankit Goyal, Stan Birchfield +3
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware…
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Joshua Bagajo, Clemens Schwarke, Victor Klemm +5
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from…
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations
Ezra Ameperosa, Jeremy A. Collins, Mrinal Jain +1
Imitation learning in robotics faces significant challenges in generalization due to the complexity of robotic environments and the high cost of data collection. We introduce RoCoD…
Discovering Robotic Interaction Modes with Discrete Representation Learning
Liquan Wang, Ankit Goyal, Haoping Xu +1
Human actions manipulating articulated objects, such as opening and closing a drawer, can be categorized into multiple modalities we define as interaction modes. Traditional robot…
SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation
Zihan Zhou, Animesh Garg, Dieter Fox +2
Robot learning has proven to be a general and effective technique for programming manipulators. Imitation learning is able to teach robots solely from human demonstrations but is b…
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Tongzhou Mu, Yijie Guo, Jie Xu +4
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datas…
Fast Explicit-Input Assistance for Teleoperation in Clutter
Nick Walker, Xuning Yang, Animesh Garg +3
The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor pre…