Publication
Blind Dexterity: Whole-Body Humanoid Manipulation via Pure Proprioception
Aditya Bhatt; Oleg Kaidanov; Puze Liu; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2608.29487, Pages 1-9, arXiv, 2026.
Abstract
We present blind, whole-body manipulation skills
on a Unitree G1 humanoid using only onboard proprioception,
without cameras, markers, force-torque, or tactile sensors. De-
spite this minimal sensing, the trained policies exhibit surprising
capability across qualitatively different tasks: push-resilient
bipedal walking without IMU feedback, active soccer ball
trapping with a foot, seeking and lifting a suitcase by its handle,
and mounting a randomly positioned skateboard.
We argue that these capabilities arise from a key underappre-
ciated signal: the way the joint encoder readouts evolve under
purposeful compliant contact, effectively forming a whole-body
tactile channel. By generating contact-rich motions, the trained
policies actively probe the environment; as a result, task-
relevant object state (e.g. pose) becomes increasingly decodable
from short proprioceptive histories. We expose this information
using compact task-specific state estimators trained alongside,
but fully separately from, the policies; their prediction errors
decrease rapidly after informative contact.
Our results indicate that joint encoder-based proprioception,
combined with compliant actuation—now widely available on
commercial robots and low-cost motors—is already a strong,
practical substrate for whole-body dexterous manipulation and
interactive perception, and therefore a natural foundation on
which richer sensing can be layered.