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Rotating without Seeing: Towards In-hand Dexterity through Touch
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Tactile information plays a critical role in human dexterity. It reveals useful contact information that may not be inferred directly from vision. In fact, humans can even perform in-hand dexterous manipulation without using vision. Can we enable the same ability for the multi-finger robot hand? In this paper, we present Touch Dexterity, a new system that can perform in-hand object rotation using only touching without seeing the object. Instead of relying on precise tactile sensing in a small region, we introduce a new system design using dense binary force sensors (touch or no touch) overlaying one side of the whole robot hand (palm, finger links, fingertips). Such a design is low-cost, giving a larger coverage of the object, and minimizing the Sim2Real gap at the same time. We train an in-hand rotation policy using Reinforcement Learning on diverse objects in simulation. Relying on touch-only sensing, we can directly deploy the policy in a real robot hand and rotate novel objects that are not presented in training. Extensive ablations are performed on how tactile information help in-hand manipulation.Our project is available at https://touchdexterity.github.io.
Forward citations
Cited by 13 Pith papers
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LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing
LocoTouch trains a quadrupedal policy that uses a 221-taxel tactile back to balance and transport unsecured cylindrical objects, transferring zero-shot to a real Unitree Go1.
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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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PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientat...
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Pixel2Catch: Multi-Agent Sim-to-Real Transfer for Agile Manipulation with a Single RGB Camera
Pixel2Catch shows that pixel-level bounding-box cues from one RGB camera, with separate arm and hand reinforcement-learning policies, are enough to catch thrown objects in the real world.
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Tactile MNIST: Benchmarking Active Tactile Perception
The authors release a Gymnasium-compatible benchmark with four active tactile tasks, 13,580 3D digit models, and 153,600 real touches on 600 printed digits.
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DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
DexMachina uses decaying virtual object controllers as a curriculum to train bimanual dexterous policies that track demonstrated object states, and reports large gains over baselines on a new six-hand benchmark.
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Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
A force-guided attention module and future-force prediction auxiliary task improve visuo-tactile fusion for dexterous manipulation, reaching 93% average success in real robot trials.
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A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
IKER uses VLM-generated keypoint rewards to train manipulation policies in simulation that transfer to a real robot, enabling multi-step tasks and replanning.
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CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World
CordViP achieves strong real-world dexterous manipulation by feeding a diffusion policy with pose-tracked 3D object models and hand point clouds, pretrained on contact maps and arm-hand coordination.
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Going with the Flow: Koopman Behavioral Models as Pseudo Planners for Visuo-Motor Dexterity
A single learned linear Koopman model over coupled visual and proprioceptive states generates full-horizon dexterous manipulation plans and triggers replanning when its own visual predictions diverge from reality.
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Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
A hierarchical RL-MPC framework with a 'contact intention' interface achieves data-efficient, robust non-prehensile manipulation that transfers zero-shot to a real robot.
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Detecting Reading-Induced Confusion Using EEG and Eye Tracking
Multimodal EEG plus eye tracking classifies reading-induced confusion at 77.3% average weighted accuracy, beating unimodal models by 4-22%, in an 11-participant study.
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
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