Pith Number

pith:D3U3Q7PA

pith:2021:D3U3Q7PAPZWVPO77XIESLGIKLL

not attested not anchored not stored refs resolved

RMA: Rapid Motor Adaptation for Legged Robots

Ashish Kumar, Deepak Pathak, Jitendra Malik, Zipeng Fu

A two-part algorithm lets quadruped robots adapt motor control to new terrains and dynamics in fractions of a second.

arxiv:2107.04034 v1 · 2021-07-08 · cs.LG · cs.AI · cs.CV · cs.RO

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Claims

C1strongest claim

The combination of these components enables the robot to adapt to novel situations in fractions of a second... RMA shows state-of-the-art performance across diverse real-world as well as simulation experiments.

C2weakest assumption

That the distribution of terrains and dynamics encountered in the varied terrain generator during simulation training is sufficiently representative of the real-world test conditions so that the adaptation module generalizes without any real-world fine-tuning.

C3one line summary

RMA lets legged robots adapt to unseen terrains and conditions in under a second by pairing a base policy with a learned adaptation module trained entirely in simulation.

References

64 extracted · 64 resolved · 1 Pith anchors

[1] Surrogate-based aerodynamic design optimization: Use of surrogates in aerodynamic design optimization 2009

[2] Rapidly exponentially stabilizing control lyapunov functions and hybrid zero dynamics 2014

[3] Fast online trajectory optimization for the bipedal robot cassie 2018

[4] Design and Control of Small Legged Robots 2018

[5] Mit chee- tah 3: Design and control of a robust, dynamic quadruped robot 2018

Formal links

2 machine-checked theorem links

Cited by

26 papers in Pith

An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors

CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing

Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning

Receipt and verification

First computed	2026-05-17T23:38:47.735548Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1ee9b87de07e6d57bbffba0925990a5addc32c8df201ebe5642e74041f3e15ff

Aliases

arxiv: 2107.04034 · arxiv_version: 2107.04034v1 · doi: 10.48550/arxiv.2107.04034 · pith_short_12: D3U3Q7PAPZWV · pith_short_16: D3U3Q7PAPZWVPO77 · pith_short_8: D3U3Q7PA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/D3U3Q7PAPZWVPO77XIESLGIKLL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1ee9b87de07e6d57bbffba0925990a5addc32c8df201ebe5642e74041f3e15ff

Canonical record JSON

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      "cs.RO"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2021-07-08T17:59:59Z",
    "title_canon_sha256": "af58f04187acd903e8c8deb0fde04828c66b43cc9726e471db6dac6efc5bc267"
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