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Found 21 papers, 4 papers with code
Problem Space Transformations for Generalisation in Behavioural Cloning
The combination of behavioural cloning and neural networks has driven significant progress in robotic manipulation.
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Under regularity assumptions on the constraints and dynamics, we show that ActSafe guarantees safety during learning while also obtaining a near-optimal policy in finite time.
PokeFlex: A Real-World Dataset of Deformable Objects for Robotics
Using different data modalities, we demonstrated a use case for the PokeFlex dataset in online 3D mesh reconstruction.
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots.
NeoRL: Efficient Exploration for Nonepisodic RL
We study the problem of nonepisodic reinforcement learning (RL) for nonlinear dynamical systems, where the system dynamics are unknown and the RL agent has to learn from a single trajectory, i. e., without resets.
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers
This difficulty arises from the requirement to pinpoint vulnerabilities across a long-tailed distribution within a high-dimensional, temporally sequential space.
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces
We propose a self-supervised approach for learning physics-based subspaces for real-time simulation.
Bridging the Sim-to-Real Gap with Bayesian Inference
We present SIM-FSVGD for learning robot dynamics from data.
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL) algorithm designed to efficiently adapt control policies to changing dynamics.
Neural Metamaterial Networks for Nonlinear Material Design
Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond.
Ungar $\unicode{x2013}$ A C++ Framework for Real-Time Optimal Control Using Template Metaprogramming
We present Ungar, an open-source library to aid the implementation of high-dimensional optimal control problems (OCPs).
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms.
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability.
Efficient Learning of High Level Plans from Play
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals.
Gradient-Based Trajectory Optimization With Learned Dynamics
In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car, and gives good performance in combination with trajectory optimization methods.
Learning Solution Manifolds for Control Problems via Energy Minimization
We formulate the learning of solution manifolds as a minimization of the energy terms of a control objective integrated over the space of problems of interest.
Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together
Spatial computing -- the ability of devices to be aware of their surroundings and to represent this digitally -- offers novel capabilities in human-robot interaction.
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations.
PODS: Policy Optimization via Differentiable Simulation
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles.
Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework.
