EFGCL adds external assistive forces during RL training to let legged robots physically experience successful dynamic motions early, accelerating learning of jumps and flips by about 2x and enabling behaviors conventional methods cannot acquire.
Deepmimic: Example-guided deep reinforcement learning of physics-based char- acter skills
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 7years
2026 7verdicts
UNVERDICTED 7roles
background 1polarities
background 1representative citing papers
Diff-CAST replaces GAN discriminators with diffusion-based priors and adds symmetric command conditioning plus constrained RL to enable versatile, drift-free, and hardware-safe quadruped locomotion.
LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
A weightlessness mechanism enables humanoid robots to dynamically relax joints for stable, contact-rich motions across diverse environments without task-specific tuning.
A musculoskeletal simulation plus reinforcement learning framework learns exoskeleton control policies that match state-of-the-art assistance profiles and improve efficiency in both healthy and simulated impaired gait.
Switch enables humanoid robots to perform agile, seamless transitions between locomotion skills via a kinematic skill graph, DRL tracking policy, and real-time graph-search scheduler.
FastDSAC adds a truncated Gaussian policy constraint to distributional actor-critic methods to preserve network plasticity and accelerate training for scalable humanoid locomotion in parallel sampling setups.
citing papers explorer
-
EFGCL: Learning Dynamic Motion through Spotting-Inspired External Force Guided Curriculum Learning
EFGCL adds external assistive forces during RL training to let legged robots physically experience successful dynamic motions early, accelerating learning of jumps and flips by about 2x and enabling behaviors conventional methods cannot acquire.
-
Constraint-Aware Diffusion Priors for High-Fidelity and Versatile Quadruped Locomotion
Diff-CAST replaces GAN discriminators with diffusion-based priors and adds symmetric command conditioning plus constrained RL to enable versatile, drift-free, and hardware-safe quadruped locomotion.
-
LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
-
Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
A weightlessness mechanism enables humanoid robots to dynamically relax joints for stable, contact-rich motions across diverse environments without task-specific tuning.
-
Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait
A musculoskeletal simulation plus reinforcement learning framework learns exoskeleton control policies that match state-of-the-art assistance profiles and improve efficiency in both healthy and simulated impaired gait.
-
Switch: Learning Agile Skills Switching for Humanoid Robots
Switch enables humanoid robots to perform agile, seamless transitions between locomotion skills via a kinematic skill graph, DRL tracking policy, and real-time graph-search scheduler.
-
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
FastDSAC adds a truncated Gaussian policy constraint to distributional actor-critic methods to preserve network plasticity and accelerate training for scalable humanoid locomotion in parallel sampling setups.