Humanoid Control

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (37 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
  15 Apr 2025 2 repositories listed Syntology ran 16 of 31 samples · 15 unverified · 31 pointer-only (licence)
  Unsupervised reinforcement learning (RL) aims at pre-training agents that can solve a wide range of downstream tasks in complex environments.
• SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
  11 Jun 2025 1 repository listed
  To that end, we introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation.
• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control
  18 Mar 2025 1 repository listed
  In this work, we present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control.
• MuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
  1 Aug 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We tackle the recently introduced benchmark for whole-body humanoid control HumanoidBench using MuJoCo MPC.
• MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
  15 Aug 2022 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  We demonstrate the utility of MoCapAct by using it to train a single hierarchical policy capable of tracking the entire MoCap dataset within dm_control and show the learned low-level component can be re-used to…
• Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
  24 Jul 2022 1 repository listed
  Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem.
• Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
  3 Jun 2022 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e.
• Learning to Brachiate via Simplified Model Imitation
  8 May 2022 1 repository listed
  Key to our method is the use of a simplified model, a point mass with a virtual arm, for which we first learn a policy that can brachiate across handhold sequences with a prescribed order.
• On the model-based stochastic value gradient for continuous reinforcement learning
  28 Aug 2020 1 repository listed
  For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.
• ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution
  5 Aug 2020 1 repository listed
  In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with…
• Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
  12 Jun 2020 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3.
• Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation
  31 Mar 2020 1 repository listed
  We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon…

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.