Search Results for author:
Found 11 papers, 4 papers with code
Speeding up 6-DoF Grasp Sampling with Quality-Diversity
We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
This framework addresses two main issues: the lack of an off-the-shelf vision module for detecting object pose and the generalization of QD trajectories to the whole robot operational space.
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets
More than 7000 reach-and-grasp trajectories have been generated with Quality-Diversity (QD) methods on 3 different arms and grippers, including parallel fingers and a dexterous hand, and tested in the real world.
Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search
This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model.
Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search
Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector.
Exploratory State Representation Learning
Our experimental results show that the approach leads to efficient exploration in challenging environments with image observations, and to state representations that significantly accelerate learning in RL tasks.
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
In particular, we show empirically that, if the mapping is smooth enough, i. e. if two close policies in the parameter space lead to similar outcomes, then diversity algorithms tend to inherit exploration properties of MP algorithms.
State Representation Learning from Demonstration
Indeed, a compact representation of such a state is beneficial to help robots grasp onto their environment for interacting.
Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception
A robot with the ability to build and adapt this interpretation process according to its own tasks and capabilities would push away the limits of what robots can achieve in a non controlled environment.
Behavioural Repertoire via Generative Adversarial Policy Networks
Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment.
Multi-objective analysis of computational models
A neurocomputational model of the Basal Ganglia brain nuclei is then considered and its most salient features according to this methodology are presented and discussed.
