Search Results for author:
Found 23 papers, 2 papers with code
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions
We formulate the estimator within a Bayesian deep learning framework, to model its uncertainty, and then train uncertainty-aware control policies by incorporating the pre-learned estimator into the reinforcement learning (RL) loop, both of which lead to significantly improved estimator and policy performance.
Learning Precise Affordances from Egocentric Videos for Robotic Manipulation
In this paper, we present a streamlined affordance learning system that encompasses data collection, effective model training, and robot deployment.
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning
Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers.
Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation
We validate the approach on the wiping task using sponges with different stiffness and surface friction.
Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration
Developing robot controllers capable of achieving dexterous nonprehensile manipulation, such as pushing an object on a table, is challenging.
A behavioural transformer for effective collaboration between a robot and a non-stationary human
On the basis of this framework, we developed Behaviour-Transform (BeTrans).
ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction
However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i. e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware.
Agile Maneuvers in Legged Robots: a Predictive Control Approach
To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models.
AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments
Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches.
PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time
Dynamic environments that include unstructured moving objects pose a hard problem for Simultaneous Localization and Mapping (SLAM) performance.
A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots
In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality.
Robotics
RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects
Here, we propose to treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components.
Robotics
Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts
We demonstrate our method on a cart-pole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality.
A Feasibility-Driven Approach to Control-Limited DDP
Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization.
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
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
The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces
To enable this to happen, the remote operator will need a high level of situation awareness and key to this is the transparency of what the autonomous systems are doing and why.
A Framework for Testing Identifiability of Bayesian Models of Perception
Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data.
An Approximate Inference Approach to Temporal Optimization in Optimal Control
Algorithms based on iterative local approximations present a practical approach to optimal control in robotic systems.
Bayesian Kernel Shaping for Learning Control
In this paper, we focus on nonparametric regression and introduce a Bayesian formulation that, with the help of variational approximations, results in an EM-like algorithm for simultaneous estimation of regression and kernel parameters.
Unifying the Sensory and Motor Components of Sensorimotor Adaptation
Adaptation of visually guided reaching movements in novel visuomotor environments (e. g. wearing prism goggles) comprises not only motor adaptation but also substantial sensory adaptation, corresponding to shifts in the perceived spatial location of visual and proprioceptive cues.
Multi-task Gaussian Process Learning of Robot Inverse Dynamics
The inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for adaptive control.
