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Found 17 papers, 7 papers with code
Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation
Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks.
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors
We demonstrate that HandyPriors attains comparable or superior results in the pose estimation task, and that the differentiable physics module can predict contact information for pose refinement.
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes the hybrid optimization problem into a continuous domain.
Inferring Articulated Rigid Body Dynamics from RGBD Video
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
In this work, we present DiSECt: the first differentiable simulator for cutting soft materials.
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.
Robotics
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.
Confidence-rich grid mapping
Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments.
Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots
Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment.
Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking
Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment.
Interactive Differentiable Simulation
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.
Scaling simulation-to-real transfer by learning composable robot skills
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
