An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
Learning agile and dynamic motor skills for legged robots.Science Robotics, 4(26):eaau5872, 2019
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 7roles
background 2polarities
background 2representative citing papers
SixthSense infers whole-body contact events and wrenches in humanoids from proprioception and IMU data alone by tokenizing histories and estimating a sparse contact-event flow with conditional flow matching.
GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for consistent environments.
Morphology-conditioned quadrupedal world model enables zero-shot generalization to new robot embodiments for locomotion tasks.
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
MoE-based locomotion policy with RoboGauge metrics achieves reliable sim-to-real transfer, enabling robust quadrupedal walking on challenging unseen terrains up to 4 m/s.
A multi-stage RL curriculum produces a unified whole-body controller enabling humanoid robots to sustain badminton rallies in simulation and return shuttles at up to 19.1 m/s in real hardware, with both EKF-based and prediction-free variants.
citing papers explorer
-
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
An adaptive smooth Tchebycheff controller for multi-objective RL lets agents reach non-convex Pareto regions in robotic tasks while avoiding the instability of static non-linear scalarizations.
-
SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids
SixthSense infers whole-body contact events and wrenches in humanoids from proprioception and IMU data alone by tokenizing histories and estimating a sparse contact-event flow with conditional flow matching.
-
GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning
GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for consistent environments.
-
Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning
Morphology-conditioned quadrupedal world model enables zero-shot generalization to new robot embodiments for locomotion tasks.
-
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
-
Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion
MoE-based locomotion policy with RoboGauge metrics achieves reliable sim-to-real transfer, enabling robust quadrupedal walking on challenging unseen terrains up to 4 m/s.
-
Humanoid Whole-Body Badminton via Multi-Stage Reinforcement Learning
A multi-stage RL curriculum produces a unified whole-body controller enabling humanoid robots to sustain badminton rallies in simulation and return shuttles at up to 19.1 m/s in real hardware, with both EKF-based and prediction-free variants.