DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies
Tony Tao, Mohan Kumar Srirama, Jason Liu, Kenneth Shaw, Deepak Pathak · 2025
Fig. 1: DexWild enables dexterous policies to generalize to new objects, scenes, and embodiments.This is achieved by leveraging large-scale, real-world human embodiment data collected in many scenes and co-trained with a smaller robot embodiment dataset for grounding.Abstract-Large-scale, diverse robot datasets have emerged as a promising path toward enabling dexterous manipulation policies to generalize to novel environments, but acquiring such datasets presents many challenges.While teleoperation provides highfidelity datasets, its high cost limits its scalability.Instead, what if people could use their own hands, just as they do in everyday life, to collect data?In DexWild, a diverse team of data collectors uses their hands to collect hours of interactions across a multitude of environments and objects.To record this data, we create DexWild-System, a low-cost, mobile, and easy-to-use device.The DexWild learning framework co-trains on both human and robot demonstrations, leading to improved performance compared to training on each dataset individually.This combination results in robust robot policies capable of generalizing to novel environments, tasks, and embodiments with minimal additional robot-specific data.Experimental results demonstrate that DexWild significantly improves performance, achieving a 68.5% success rate in unseen environments-nearly four times higher than policies trained with robot data only-and offering 5.8× better cross-embodiment generalization.Video results, codebases, and instructions at https: //dexwild.github.io