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Found 31 papers, 6 papers with code
ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching
Despite the impressive results of deep generative models in complex manipulation tasks, the absence of a representation that encodes intricate spatial relationships between observations and actions often limits spatial generalization, necessitating large amounts of demonstrations.
Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields
Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning.
Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
In this survey, we aim to provide a unified and comprehensive review of the last year's progress in the use of deep generative models in robotics.
MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations
In this work, we propose a novel approach for learning a shared latent space representation for HRIs from demonstrations in a Mixture of Experts fashion for reactively generating robot actions from human observations.
Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers
Bimanual handovers are crucial for transferring large, deformable or delicate objects.
Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation
To overcome this shortcoming, we study the idea of replacing the RGB camera with an event-based camera and introduce a new event-based optical tactile sensor called Evetac.
Learning Multimodal Latent Dynamics for Human-Robot Interaction
The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability.
Domain Randomization via Entropy Maximization
Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL).
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i. e., rational strategy, corresponds to a Nash equilibrium.
Active-Perceptive Motion Generation for Mobile Manipulation
In this work, we introduce an active perception pipeline for mobile manipulators to generate motions that are informative toward manipulation tasks, such as grasping in unknown, cluttered scenes.
Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering
A significant challenge for real-world robotic manipulation is the effective 6DoF grasping of objects in cluttered scenes from any single viewpoint without the need for additional scene exploration.
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Long-horizon task planning is essential for the development of intelligent assistive and service robots.
Hierarchical Policy Blending As Optimal Transport
We present hierarchical policy blending as optimal transport (HiPBOT).
Active Exploration for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years.
MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction
Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative Human-Robot Interactions (HRI).
Hierarchical Policy Blending as Inference for Reactive Robot Control
To combine the benefits of reactive policies and planning, we propose a hierarchical motion generation method.
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
Two original information-theoretic losses, computed from local entropy, guide our model to discover consistent keypoint representations; a loss that maximizes the spatial information covered by the keypoints and a loss that optimizes the keypoints' information transportation over time.
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction
Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment.
SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
In this work, we focus on learning SE(3) diffusion models for 6DoF grasping, giving rise to a novel framework for joint grasp and motion optimization without needing to decouple grasp selection from trajectory generation.
Learning Implicit Priors for Motion Optimization
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization.
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints are satisfied, e. g., a collision-free trajectory from one state to another.
Regularized Deep Signed Distance Fields for Reactive Motion Generation
Autonomous robots should operate in real-world dynamic environments and collaborate with humans in tight spaces.
Robot Learning of Mobile Manipulation with Reachability Behavior Priors
Finally, we zero-transfer our learned 6D fetching policy with BHyRL to our MM robot TIAGo++.
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery
Robot assembly discovery is a challenging problem that lives at the intersection of resource allocation and motion planning.
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance.
Model-based Reinforcement Learning
Model Predictive Control
+3
Extended Tree Search for Robot Task and Motion Planning
Moreover, we effectively combine this skeleton space with the resultant motion variable spaces into a single extended decision space.
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Parameterized movement primitives have been extensively used for imitation learning of robotic tasks.
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation
Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping.
How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking
We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects.
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task
In this work we tackle the problem of child engagement estimation while children freely interact with a robot in their room.
LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator
In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors.
