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Found 33 papers, 12 papers with code
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action generation, demonstrating improved performance on various complex, long-horizon manipulation tasks.
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
However, the open-world mobile manipulation (OWMM) task remains a challenge due to the need for generalization to open-ended instructions and environments, as well as the systematic complexity to integrate high-level decision making with low-level robot control based on both global scene understanding and current agent state.
Adaptive Wall-Following Control for Unmanned Ground Vehicles Using Spiking Neural Networks
Unmanned ground vehicles operating in complex environments must adaptively adjust to modeling uncertainties and external disturbances to perform tasks such as wall following and obstacle avoidance.
Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Models
Humans possess an extraordinary ability to understand and execute complex manipulation tasks by interpreting abstract instruction manuals.
FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model
We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only language and vision inputs.
EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents
Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone.
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
Key-Grid achieves the state-of-the-art performance on the semantic consistency and position accuracy of keypoints.
TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach
We introduce the Hierarchical Feature Matching approach to estimate a sequence of tie's meshes from the demonstration video.
Learning Reward for Robot Skills Using Large Language Models via Self-Alignment
Learning reward functions remains the bottleneck to equip a robot with a broad repertoire of skills.
ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots
Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis.
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation
RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object.
SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes
We present SoftMAC, a differentiable simulation framework that couples soft bodies with articulated rigid bodies and clothes.
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills.
Generalizable Long-Horizon Manipulations with Large Language Models
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks.
GAMMA: Generalizable Articulation Modeling and Manipulation for Articulated Objects
Results show that GAMMA significantly outperforms SOTA articulation modeling and manipulation algorithms in unseen and cross-category articulated objects.
Jade: A Differentiable Physics Engine for Articulated Rigid Bodies with Intersection-Free Frictional Contact
Compared to existing differentiable simulations, Jade offers features including intersection-free collision simulation and stable LCP solutions for multiple frictional contacts.
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering
Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL.
Deformable Object Manipulation
Model-based Reinforcement Learning
+3
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment
Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape.
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning
We apply our system to perform articulated object manipulation tasks, both in the simulation and the real world.
Learning to Regrasp by Learning to Place
In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses.
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation
In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably.
GRAC: Self-Guided and Self-Regularized Actor-Critic
One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function.
Generative 3D Part Assembly via Dynamic Graph Learning
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
Design and Control of Roller Grasper V2 for In-Hand Manipulation
The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and reorienting of grasped objects.
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
Learning to Scaffold the Development of Robotic Manipulation Skills
To combat these factors and achieve more robust manipulation, humans actively exploit contact constraints in the environment.
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
ClusterNet: 3D Instance Segmentation in RGB-D Images
We show that our method generalizes well on real-world data achieving visually better segmentation results.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
