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Found 21 papers, 8 papers with code
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
While Vision-Language Models (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend physical phenomena remains extremely limited.
VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation
Bimanual manipulation is critical to many robotics applications.
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features.
DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning
Then, students learn by running either offline RL or by using teacher data in combination with a small amount of self-generated data.
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior.
VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
We build upon the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different sequential fabric manipulation tasks with a single goal-conditioned policy.
Automating Surgical Peg Transfer: Calibration with Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans
Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS).
Robotics
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag".
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations.
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
We explore learning pixelwise correspondences between images of deformable objects in different configurations.
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics.
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery.
Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks
Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis.
Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot
We report experimental results for a handover-free version of the peg transfer task, performing 20 and 5 physical episodes with single- and bilateral-arm setups, respectively.
Robotics
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
In 180 physical experiments with the da Vinci Research Kit (dVRK) surgical robot, RGBD policies trained in simulation attain coverage of 83% to 95% depending on difficulty tier, suggesting that effective fabric smoothing policies can be learned from an algorithmic supervisor and that depth sensing is a valuable addition to color alone.
Risk Averse Robust Adversarial Reinforcement Learning
In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary.
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points.
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet.
Robotics
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data.
Fast Parallel SAME Gibbs Sampling on General Discrete Bayesian Networks
A fundamental task in machine learning and related fields is to perform inference on Bayesian networks.
