Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.
Kaup et al.,A review of nine physics engines for reinforcement learning research
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
PBD-R adds a momentum-conservation constraint to position-based dynamics to deliver physically accurate rigid-body dynamics while remaining computationally lighter than MuJoCo.
A SHAP analysis framework is introduced to decompose configuration impacts on RL generalization and guide selection for improved performance in robotics.
citing papers explorer
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Trajectory-based actuator identification via differentiable simulation
Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.
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Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation
PBD-R adds a momentum-conservation constraint to position-based dynamics to deliver physically accurate rigid-body dynamics while remaining computationally lighter than MuJoCo.
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Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
A SHAP analysis framework is introduced to decompose configuration impacts on RL generalization and guide selection for improved performance in robotics.