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Found 40 papers, 10 papers with code
Stein Variational Evolution Strategies
Stein Variational Gradient Descent (SVGD) is a highly efficient method to sample from an unnormalized probability distribution.
Amortized Active Learning for Nonparametric Functions
Active learning (AL) is a sequential learning scheme aiming to select the most informative data.
NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Sampling
Generating diverse samples under hard constraints is a core challenge in many areas.
Global Safe Sequential Learning via Efficient Knowledge Transfer
As transferable source knowledge is often available in safety critical experiments, we propose to consider transfer safe sequential learning to accelerate the learning of safety.
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks.
Ranked #2 on
Visual Question Answering (VQA)
on OK-VQA
Reinforcement Learning with Neural Radiance Fields
This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performance of RL compared to other learned representations or even low-dimensional, hand-engineered state information.
Learning Multi-Object Dynamics with Compositional Neural Radiance Fields
We present a method to learn compositional multi-object dynamics models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks.
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
We show that representing objects as signed-distance fields not only enables to learn and represent a variety of models with higher accuracy compared to point-cloud and occupancy measure representations, but also that SDF-based models are suitable for optimization-based planning.
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation
Robotic manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e. g., grasp, placement, tool-use, etc.
Active Learning in Gaussian Process State Space Model
We investigate active learning in Gaussian Process state-space models (GPSSM).
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.
A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures
After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i. e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time.
GraspME -- Grasp Manifold Estimator
To this extend, we define grasp manifolds via a set of key points and locate them in images using a Mask R-CNN backbone.
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping
We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.
Visualization of Nonlinear Programming for Robot Motion Planning
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving.
Motion Planning
Robotics
Human-Computer Interaction
Numerical Analysis
Numerical Analysis
H.5.2; G.1.6
Plan-Based Asymptotically Equivalent Reward Shaping
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.
MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
Hence, in this paper, we present a novel dataset of full-body motion for everyday manipulation tasks, which includes the above.
Robotics
Multilevel Motion Planning: A Fiber Bundle Formulation
Those methods exploit the structure of fiber bundles through the use of bundle primitives.
Robotics
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning
By selecting a subgraph with a favorable structure, we construct a simplified Markov Decision Process for which exact Q-values can be computed efficiently as more data comes in.
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector.
Optimized Directed Roadmap Graph for Multi-Agent Path Finding Using Stochastic Gradient Descent
It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation.
Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning
Physical reasoning is a core aspect of intelligence in animals and humans.
Robotics
Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
Human movement prediction is difficult as humans naturally exhibit complex behaviors that can change drastically from one environment to the next.
Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
Those minima are important to visualize to let a user guide, prevent or predict motions.
Deep Workpiece Region Segmentation for Bin Picking
For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor.
Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications
Motion planning problems can be simplified by admissible projections of the configuration space to sequences of lower-dimensional quotient-spaces, called sequential simplifications.
Trajectory-Based Off-Policy Deep Reinforcement Learning
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.
Kinematic Morphing Networks for Manipulation Skill Transfer
The transfer of a robot skill between different geometric environments is non-trivial since a wide variety of environments exists, sensor observations as well as robot motions are high-dimensional, and the environment might only be partially observed.
Probabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands.
Identification of Unmodeled Objects from Symbolic Descriptions
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand.
Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down
Bayesian Optimization (BO) has become a core method for solving expensive black-box optimization problems.
The Advantage of Cross Entropy over Entropy in Iterative Information Gathering
Gathering the most information by picking the least amount of data is a common task in experimental design or when exploring an unknown environment in reinforcement learning and robotics.
Newton methods for k-order Markov Constrained Motion Problems
This is a documentation of a framework for robot motion optimization that aims to draw on classical constrained optimization methods.
Robotics
Planning with Noisy Probabilistic Relational Rules
They are compact and generalize over world instantiations.
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress
Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error.
Model-based Reinforcement Learning
reinforcement-learning
+2
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.
Modelling motion primitives and their timing in biologically executed movements
Inference of the shape and the timing of primitives can be done using a factorial HMM based model, allowing the handwriting to be represented in primitive timing space.
