REVIEW 14 cited by
Learning Agile Robotic Locomotion Skills by Imitating Animals
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many complex behaviors, building such controllers involves a time-consuming and difficult development process, often requiring substantial expertise of the nuances of each skill. Reinforcement learning provides an appealing alternative for automating the manual effort involved in the development of controllers. However, designing learning objectives that elicit the desired behaviors from an agent can also require a great deal of skill-specific expertise. In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals. We show that by leveraging reference motion data, a single learning-based approach is able to automatically synthesize controllers for a diverse repertoire behaviors for legged robots. By incorporating sample efficient domain adaptation techniques into the training process, our system is able to learn adaptive policies in simulation that can then be quickly adapted for real-world deployment. To demonstrate the effectiveness of our system, we train an 18-DoF quadruped robot to perform a variety of agile behaviors ranging from different locomotion gaits to dynamic hops and turns.
Forward citations
Cited by 14 Pith papers
-
Deep Sensorimotor Control by Imitating Predictive Models of Human Motion
A predictive model of human hand motion, trained on human interaction data, can reward a robot policy for tracking predicted future keypoints and enable learning of dexterous manipulation from sparse rewards.
-
What Matters in Humanoid General Motion Tracking? An Empirical Study
A controlled ablation of humanoid motion-tracking pipelines shows that explicit reference joint velocities and a short observation history improve tracking, while residual actions and teacher-student training yield on...
-
StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots
StairMaster trains an RL policy that lets a Unitree Go2 quadruped climb hollow stairs up to 55 degrees via zero-shot sim-to-real transfer using cross-attention, SRU memory, and active-perception rewards.
-
DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization
DynaRetarget's SBTO, which incrementally extends the optimization horizon while warm-starting from shorter solutions, refines kinematic humanoid demonstrations into dynamically consistent whole-body motions with highe...
-
Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer
A model-homotopy curriculum that gradually redistributes mass and inertia from a single-rigid-body model to full-body dynamics lets a quadruped learn flips and wall-assisted maneuvers faster and more stably than direc...
-
Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
A particle-filter-based adaptive sampling of morphologies and wide PD-gain randomization yields a single quadruped locomotion policy that transfers zero-shot to ANYmal hardware.
-
Musculoskeletal simulation of limb movement biomechanics in Drosophila melanogaster
An anatomically grounded, data-driven muscle model of Drosophila legs is built in OpenSim and MuJoCo and used to replay behaviors, predict synergies, and test passive joint effects.
-
CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Causal Diffusion Policy adds historical action conditioning and attention cache sharing to diffusion-based robot policies, improving success rates on most tested manipulation tasks under degraded observations.
-
CARoL: Context-aware Adaptation for Robot Learning
CARoL measures task similarity by state-transition prediction errors and uses those similarities to weight prior policies, value functions, or actor-critic knowledge when adapting to a new task.
-
PRISM: Polynomial Representations for Interaction-Structured Motor Control
Explicit low-degree factorized polynomial proprioceptive features improve robot RL and imitation policies beyond matched-capacity MLPs and induce sensorless compliance-like contact behavior in simulation.
-
ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
Decoupled arm–hand retargeting with anthropomorphic arm-plane constraints and polyhedral contact invariants raises real-robot dexterous-task success to 75.8% versus 61.6% and 50.8% for OKAMI and ORION.
-
Reference Free Platform Adaptive Locomotion for Quadrupedal Robots using a Dynamics Conditioned Policy
A single dynamics-conditioned RL policy transfers zero-shot across quadrupeds from 12 kg to 50 kg, and diverse reference robots during training clearly improve tracking.
-
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
A hierarchical quadruped controller uses online optimization over the low-level policy's value function to choose footstep targets, improving normalized reward and reducing collisions over an end-to-end baseline witho...
-
Spatial-Temporal Aware Visuomotor Diffusion Policy Learning
A diffusion-based visuomotor policy gains 3D and 4D scene awareness from a dynamic Gaussian world model, improving simulated and real robot manipulation success rates.
Discussion (0). Continue with ORCID to comment.