11 papers
The science case for an in-situ measurement platform in the Venus clouds
Maureen Cohen, Joanna Egan, Martin Airey +14
The next step for Venus exploration after the remote sensing missions of the early 2030s will be in-situ aerial platforms, the Venus equivalent of rovers for Mars. Sophisticated Ve…
SituHOI: Scalable Capture of Scene-Situated Full-Body Human-Object Interaction
Qifan Wang, Qihang Fang, Boao Shi +4
Modeling human-object interaction (HOI) in real environments requires jointly representing whole-body motion, manipulated objects, and scene geometry, yet existing datasets trade o…
The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction
Xunzhe Zhou, Yiyang Cai, Fengyi Wang +9
Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at hand. Current robot policies inste…
Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning
Qian Luo, Ruizhe Liu, Pei Zhou +2
We study how trajectory-shape diversity in demonstrations affects imitation learning (IL) performance across models, tasks, and data scales. We introduce Geometric Entropy (H_G), a…
DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
Hanxiang Ren, Pei Zhou, Xunzhe Zhou +1
Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entanglement provides a pathway for ob…
Reinforcing Human Behavior Simulation via Verbal Feedback
Weiwei Sun, Xuhui Zhou, Jiarui Liu +13
Humans learn social norms and behaviors from verbal feedback (e.g., a parent saying "that was rude" or a friend explaining "here's why that hurt"). Yet, learning from feedback for…
DexHoldem: An Agentic Robotics Benchmark for Dexterous Manipulation in Texas Hold'em
Feng Chen, Tianzhe Chu, Li Sun +6
Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing scene (e.g. a tabletop), choose a context-…
Hyper-GoalNet: Goal-Conditioned Manipulation Policy Learning with HyperNetworks
Pei Zhou, Wanting Yao, Qian Luo +2
Goal-conditioned policy learning for robotic manipulation presents significant challenges in maintaining performance across diverse objectives and environments. We introduce Hyper-…
HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data
Ruizhe Liu, Pei Zhou, Qian Luo +4
Effective generalization in robotic manipulation requires representations that capture invariant patterns of interaction across environments and tasks. We present a self-supervised…
Learning Human-Humanoid Coordination for Collaborative Object Carrying
Yushi Du, Yixuan Li, Baoxiong Jia +5
Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been ex…
MaxMI: A Maximal Mutual Information Criterion for Manipulation Concept Discovery
Pei Zhou, Yanchao Yang
We aim to discover manipulation concepts embedded in the unannotated demonstrations, which are recognized as key physical states. The discovered concepts can facilitate training ma…