7 papers · 1 filter
FACT: Failure-Aware Causal Training for World-Action Models
Quanquan Peng, Yutong Liang, Rui Yan +2
Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on the future-prediction ability…
RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
Hanan Gani, Tejal Kulkarni, Madhoolika Chodavarapu +2
Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional.…
Generating Robot Hands from Human Demonstrations
Sha Yi, Nicklas Hansen, Xueqian Bai +3
Robot learning has advanced rapidly in learning control, but learning the physical body of a robot remains much more difficult because jointly searching over design and control cre…
Learning to Design Soft Hands using Reward Models
Xueqian Bai, Nicklas Hansen, Adabhav Singh +5
Soft robotic hands promise to provide compliant and safe interaction with objects and environments. However, designing soft hands to be both compliant and functional across diverse…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…