Search Results for author:
Found 45 papers, 17 papers with code
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements
The robot pipeline integrates with a logging module and an online database of objects, containing over 24, 000 measurements of 63 objects with different grippers.
Automated Feature Selection for Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations.
Online Learning of Human Constraints from Feedback in Shared Autonomy
We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions.
The Role of Higher-Order Cognitive Models in Active Learning
Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence.
Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks
This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Benchmarking the Sim-to-Real Gap in Cloth Manipulation
The dataset is collected by performing a dynamic as well as a quasi-static cloth manipulation task involving contact with a rigid table.
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle.
QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation
To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives.
Trajectory Optimization on Matrix Lie Groups with Differential Dynamic Programming and Nonlinear Constraints
Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and the optimization of control policies on these manifolds is a fundamental problem.
Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving
Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context.
Co-Imitation: Learning Design and Behaviour by Imitation
To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state distributions of the demonstrator.
Bayesian Floor Field: Transferring people flow predictions across environments
Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in.
Online vs. Offline Adaptive Domain Randomization Benchmark
However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
Active Visuo-Haptic Object Shape Completion
We also propose a new simulation environment for this purpose.
A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms
Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency.
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
In this work, we introduce a novel learning algorithm called SafeAPT that leverages a diverse repertoire of policies evolved in the simulation and transfers the most promising safe policy to the real robot through episodic interaction.
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult.
Manipulation of Granular Materials by Learning Particle Interactions
In this paper, we propose to use a graph-based representation to model the interaction dynamics of the material and rigid bodies manipulating it.
Vision Transformer for Learning Driving Policies in Complex Multi-Agent Environments
Driving in a complex urban environment is a difficult task that requires a complex decision policy.
MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization
We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in critic's network architecture and propose a new framework to use a meta-trajectory to train the recurrent layer.
Evolving-Graph Gaussian Processes
Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains.
Affine Transport for Sim-to-Real Domain Adaptation
In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning
To do this, we train an encoder-decoder deep neural network to predict multiple application-relevant factors such as the trajectories of other agents and the ego car.
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning.
DDGC: Generative Deep Dexterous Grasping in Clutter
Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
Autoencoding Slow Representations for Semi-supervised Data Efficient Regression
We find that slow representations lead to equal or better downstream task performance and data efficiency in different experiment domains when compared to representations without slowness regularization.
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition.
Few-shot model-based adaptation in noisy conditions
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection.
Notes on the Behavior of MC Dropout
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness.
Data-efficient visuomotor policy training using reinforcement learning and generative models
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
DeFINE: Delayed Feedback based Immersive Navigation Environment for Studying Goal-Directed Human Navigation
With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior.
Efficient Latent Representations using Multiple Tasks for Autonomous Driving
Driving in the dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision policy.
Robotics
MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations
Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware.
Beyond Top-Grasps Through Scene Completion
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps.
ViTa-SLAM: A Bio-inspired Visuo-Tactile SLAM for Navigation while Interacting with Aliased Environments
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outdoor environments.
Robotics
Object Pose Estimation in Robotics Revisited
The evaluation metric is based on non-parametric probability density that is estimated from samples of a real physical setup.
From Video Game to Real Robot: The Transfer between Action Spaces
Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training.
Affordance Learning for End-to-End Visuomotor Robot Control
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide.
Robust Grasp Planning Over Uncertain Shape Completions
We present a method for planning robust grasps over uncertain shape completed objects.
Robotics
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation.
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring.
