5 papers
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…
Rethinking Demonstration Unlearning in Imitation Learning for Robotics
Jiazhuo Li, Yu Zhang, Yiming Fei +4
Imitation learning for robotics depends on human demonstrations, some of which people may later ask to remove. Retraining without them is the natural reference, but its cost grows…
D-SPEAR: Dual-Stream Prioritized Experience Adaptive Replay for Stable Reinforcement Learning in Robotic Manipulation
Yu Zhang, Karl Mason
Robotic manipulation remains challenging for reinforcement learning due to contact-rich dynamics, long horizons, and training instability. Although off-policy actor-critic algorith…
SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
Yu Zhang, Yuqi Xie, Huihan Liu +4
Imitation learning advances robot capabilities by enabling the acquisition of diverse behaviors from human demonstrations. However, large-scale datasets used for policy training of…
Multi-Task Interactive Robot Fleet Learning with Visual World Models
Huihan Liu, Yu Zhang, Vaarij Betala +4
Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse t…