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Found 76 papers, 19 papers with code
Video Prediction Models as Rewards for Reinforcement Learning
A promising approach is to extract preferences for behaviors from unlabeled videos, which are widely available on the internet.
LERF: Language Embedded Radiance Fields
Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances.
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify sparse reward functions.
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping.
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling.
SpeedFolding: Learning Efficient Bimanual Folding of Garments
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments.
Automated Pruning of Polyculture Plants
Using an overhead camera to collect data from a physical scale garden testbed, the autonomous system utilizes a learned Plant Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm to evaluate the individual plant distribution and estimate the state of the garden each day.
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.
DayDreamer: World Models for Physical Robot Learning
Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.
Multi-Object Grasping in the Plane
In physical grasping experiments comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13. 6\% higher grasp success and is 59. 9\% faster, from 212 PPH to 340 PPH.
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware.
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
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.
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models
For applications in healthcare, physics, energy, robotics, and many other fields, designing maximally informative experiments is valuable, particularly when experiments are expensive, time-consuming, or pose safety hazards.
Mechanical Search on Shelves using a Novel "Bluction" Tool
Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency.
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
AlphaGarden: Learning to Autonomously Tend a Polyculture Garden
This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1. 5m x 3. 0m physical testbed.
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects
The ability to grasp and manipulate transparent objects is a major challenge for robots.
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible to control the timing and length of interventions to both facilitate learning and limit burden on the human supervisor?
Accelerating Quadratic Optimization with Reinforcement Learning
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly.
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.
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.
Policy Gradient Bayesian Robust Optimization for Imitation Learning
Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator's reward function.
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.
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images.
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
Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search
We propose to use a 3D scene graph representation to capture the hierarchical, semantic, and geometric aspects of this problem.
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.
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".
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects
However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps.
Accelerating Grasp Exploration by Leveraging Learned Priors
The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service.
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.
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.
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce.
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
We demonstrate the use of this representation to imitate surgical suturing motions from publicly available videos of the JIGSAWS dataset.
X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions
For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object.
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.
GOMP: Grasp-Optimized Motion Planning for Bin Picking
Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH).
Robotics
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.
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.
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
Hierarchical Variational Imitation Learning of Control Programs
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
Continuous Online Learning and New Insights to Online Imitation Learning
We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP.
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
+1
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin.
Robotics
Online Learning with Continuous Variations: Dynamic Regret and Reductions
Furthermore, we show for COL a reduction from dynamic regret to both static regret and convergence in the associated EP, allowing us to analyze the dynamic regret of many existing algorithms.
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality.
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.
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry.
Ranked #1 on
Unseen Object Instance Segmentation
on WISDOM
A Fog Robotic System for Dynamic Visual Servoing
We use the system to enable robust teleoperation of a dynamic self-balancing robot from the cloud.
Learning to Optimize Join Queries With Deep Reinforcement Learning
Exhaustive enumeration of all possible join orders is often avoided, and most optimizers leverage heuristics to prune the search space.
Databases
Parametrized Hierarchical Procedures for Neural Programming
Neural programs are highly accurate and structured policies that perform algorithmic tasks by controlling the behavior of a computation mechanism.
RLlib: Abstractions for Distributed Reinforcement Learning
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.
A Berkeley View of Systems Challenges for AI
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.
Learning Robust Bed Making using Deep Imitation Learning with DART
Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions.
Robotics
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise.
Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Imitation learning is a powerful paradigm for robot skill acquisition.
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
Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact.
Robotics
DART: Noise Injection for Robust Imitation Learning
One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.
Robotics
Multi-Level Discovery of Deep Options
Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces.
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.
