Researchers introduce REGRIND, a pipeline that learns dexterous robot manipulation policies from a single human demonstration by retargeting hand-object motion to robot kinematic references and training a residual RL policy to track object-centric keypoints. The system achieves zero-shot sim-to-real transfer on multi-fingered hands for contact-rich tasks including scissors operation and screwdriver turning. The paper systematically analyzes key factors governing sim-to-real transfer in dexterous manipulation, extending a recipe previously validated for humanoid whole-body control into the harder contact-rich manipulation domain.
CoorDex is a reinforcement learning pipeline that enables humanoid robots to perform dexterous manipulation while walking, eliminating the stop-and-go pattern common in prior work. The approach trains separate privileged motion tracking teachers for body and hand, distills them into latent priors, and uses coordinated residual RL to compose them for downstream tasks. Demonstrated on a Unitree G1 humanoid with a 20-DoF WUJI hand, the system achieves non-stop bottle grasping, fridge door opening, and cube manipulation in motion. Ablations show that naive joint-space or monolithic approaches fail under the same reward budget, validating the latent-prior architecture.
Researchers introduce Mana (Manipulation Animator), a sim-to-real framework that reframes dexterous robotic manipulation as an animation problem using a coarse-to-fine pipeline of procedurally-generated grasp keyframes, motion planning, and reinforcement learning. The system requires minimal human input (under one minute per tool) and achieves zero-shot sim-to-real transfer across four articulated tools with varying joint types and scales. The work addresses a longstanding gap in dexterous robotics where articulated tool use—requiring coordination of internal degrees of freedom and contact-rich interactions—has been underexplored relative to rigid object manipulation.
OpenAI announced the training of a human-like robot hand capable of manipulating physical objects with what they describe as unprecedented dexterity. The system uses reinforcement learning to develop fine motor control in a dexterous robotic hand. This work represents an early milestone in OpenAI's robotics research program, predating their later Dactyl work on solving Rubik's cubes.
Researchers introduce DexCompose, a framework for reusing pretrained dexterous manipulation policies to perform multiple tasks simultaneously with a single robotic hand. The approach assigns explicit finger-level action ownership and trains asymmetric residual modules — a bounded stabilizer for preserving existing skill states and a context-aware residual for new task execution — to avoid destructive interference between overlapping skills. Evaluated on 16 composite manipulation tasks, DexCompose achieves a 77.4% average composite success rate, outperforming conventional policy chaining approaches.
AutoDex is an automated system for collecting physically-labeled dexterous grasping data at scale, closing the perception-execution-labeling-reset loop without human intervention. The system uses 20-camera dense perception to handle hand-object occlusion, executes collision-monitored motions, and actively resets objects between trials. Across 100 objects and two robot hand platforms, AutoDex achieves 4.8x throughput versus teleoperation and yields 76% grasp success from its validated database versus 34% for simulation-only validation. Code and data will be publicly released.
OpenAI trained neural networks to solve a Rubik's Cube using a dexterous robot hand, with training conducted entirely in simulation via reinforcement learning. A new technique called Automatic Domain Randomization (ADR) enables the system to generalize to real-world physical perturbations not seen during training. The work demonstrates that sim-to-real transfer can achieve unprecedented dexterity in manipulation tasks.
Researchers introduce Center-of-Pressure (CoP), a tactile representation grounded in physical principles designed to bridge the sim-to-real gap in contact-rich dexterous manipulation. CoP preserves dense contact information while remaining robust for sim-to-real transfer, supported by a differentiable-dynamics-based sensor calibration scheme that estimates taxel orientations without ground-truth force measurements. Evaluated on peg-in-hole insertion and ball balancing tasks, CoP-conditioned policies achieve zero-shot sim-to-real transfer on a multi-fingered robotic hand, outperforming binary-contact and raw-taxel baselines. An emergent finding is that CoP-conditioned policies implicitly encode task-relevant physical properties such as object mass.
Researchers introduce a pipeline that generates 48,000 paired vision-language-kinematics trajectories synthetically using 3D Gaussian Splatting to reconstruct indoor scenes, bypassing the need for expensive human-annotated robot data. A VLK policy trained on this data predicts whole-body kinematic trajectories from egocentric images and language instructions, which a whole-body tracker converts to physical actions. The approach is validated on a Unitree G1 humanoid performing navigation and object transport, demonstrating viable sim-to-real transfer for perception-based loco-manipulation.