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Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards
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Training robots directly from human videos is an emerging area in robotics and computer vision. While there has been notable progress with two-fingered grippers, learning autonomous tasks for multi-fingered robot hands in this way remains challenging. A key reason for this difficulty is that a policy trained on human hands may not directly transfer to a robot hand due to morphology differences. In this work, we present HuDOR, a technique that enables online fine-tuning of policies by directly computing rewards from human videos. Importantly, this reward function is built using object-oriented trajectories derived from off-the-shelf point trackers, providing meaningful learning signals despite the morphology gap and visual differences between human and robot hands. Given a single video of a human solving a task, such as gently opening a music box, HuDOR enables our four-fingered Allegro hand to learn the task with just an hour of online interaction. Our experiments across four tasks show that HuDOR achieves a 4x improvement over baselines. Code and videos are available on our website, https://object-rewards.github.io.
Forward citations
Cited by 5 Pith papers
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MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos
Trained only on unlabeled human play videos, MimicDroid lets a GR1 humanoid perform new manipulation tasks from one to three demonstration videos, with roughly twice the real-world success of prior video-conditioned methods.
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Feel the Force: Contact-Driven Learning from Humans
FeelTheForce trains a robot policy on human tactile demonstrations, predicting desired contact forces and using a PD controller to track them on the robot gripper, achieving 77% success across five force-sensitive tasks.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
A three-stage pipeline that turns keypoint tracks into discrete motion tokens, predicts them from action-free video, and decodes them into actions yields large few-shot and zero-shot policy improvements in robot manipulation.
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EgoZero: Robot Learning from Smart Glasses
Robot policies trained only on egocentric human videos from smart glasses transfer zero-shot to a Franka gripper, with 70% success across 7 manipulation tasks.
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