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Found 23 papers, 5 papers with code
Autonomously Untangling Long Cables
Cables are ubiquitous in many settings and it is often useful to untangle them.
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision
With continual learning, interventions from the remote pool of humans can also be used to improve the robot fleet control policy over time.
All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators
Large-scale semantic image annotation is a significant challenge for learning-based perception systems in robotics.
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
We then present a new algorithm, Latent Space Safe Sets (LS3), which uses this representation for long-horizon tasks with sparse rewards.
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo
However, the RGB-D baseline only grasps 35% of the hard (e. g., transparent) objects, while SimNet grasps 95%, suggesting that SimNet can enable robust manipulation of unknown objects, including transparent objects, in unknown environments.
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
We present two algorithms that enhance robust cable untangling, LOKI and SPiDERMan, which operate alongside HULK, a high-level planner from prior work.
PAC Best Arm Identification Under a Deadline
In this work, the decision-maker is given a deadline of $T$ rounds, where, on each round, it can adaptively choose which arms to pull and how many times to pull them; this distinguishes the number of decisions made (i. e., time or number of rounds) from the number of samples acquired (cost).
Disentangling Dense Multi-Cable Knots
Disentangling two or more cables requires many steps to remove crossings between and within cables.
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.
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
Untangling Dense Knots by Learning Task-Relevant Keypoints
HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97. 9% of 378 simulation experiments with an average of 12. 1 actions per trial.
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
Second, we present an algorithm for a fixed deadline setting, where we are given a time deadline and need to maximize the probability of finding the best arm.
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.
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.
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.
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
We address these challenges using interpretable deep visual representations for rope, extending recent work on dense object descriptors for robot manipulation.
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks.
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
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.
On-Policy Robot Imitation Learning from a Converging Supervisor
Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor.
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging.
Model-based Reinforcement Learning
reinforcement-learning
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