Search Results for author:
Found 18 papers, 8 papers with code
Meta-Learning via Learned Loss
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation.
Robotics
Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP).
Robotics Optimization and Control
TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
Leveraging Contact Forces for Learning to Grasp
While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.
Simultaneous Navigation and Construction Benchmarking Environments
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.
Robot Learning with Crash Constraints
We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation.
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
In this work, we introduce a generic and scalable method based on multiple shooting to learn latent representations of indirectly observed dynamical systems.
Probabilistic Depth Image Registration incorporating Nonvisual Information
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking.
A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation
This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized.
Robotics
Curious iLQR: Resolving Uncertainty in Model-based RL
In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Variable Impedance Control for Contact Sensitive Tasks
We propose learning a policy giving as output impedance and desired position in joint space and compare the performance of that approach to torque and position control under different contact uncertainties.
Learning to Explore in Motion and Interaction Tasks
In this paper we present a novel approach for efficient exploration that leverages previously learned tasks.
Mobile Construction Benchmark
We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan.
Robust walking based on MPC with viability guarantees
In this approach, instead of adding a (conservative) terminal constraint to the problem, we propose to use the measured state projected to the viability kernel in the OCP solved at each control cycle.
Robotics
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization
We present a general, two-stage reinforcement learning approach to create robust policies that can be deployed on real robots without any additional training using a single demonstration generated by trajectory optimization.
Meta Learning via Learned Loss
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
Model Based Meta Learning of Critics for Policy Gradients
In this paper we present a framework to meta-learn the critic for gradient-based policy learning.
