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Found 31 papers, 1 papers with code
Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations
In this paper, we present a novel framework for simultaneously designing trajectories of robots, objects, and contacts efficiently for contact-rich manipulation.
FDPP: Fine-tune Diffusion Policy with Human Preference
This reward is then used to fine-tune the pre-trained policy with reinforcement learning (RL), resulting in alignment of pre-trained policy with new human preferences while still solving the original task.
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Our approach, called RecoveryChaining, uses a hybrid action space, where the model-based controllers are provided as additional \emph{nominal} options which allows the recovery policy to decide how to recover, when to switch to a nominal controller and which controller to switch to even with \emph{sparse rewards}.
Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling.
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI.
Tactile Estimation of Extrinsic Contact Patch for Stable Placement
In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation.
A Virtual Reality Teleoperation Interface for Industrial Robot Manipulators
We address the problem of teleoperating an industrial robot manipulator via a commercially available Virtual Reality (VR) interface.
Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization
This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment.
Tactile-Filter: Interactive Tactile Perception for Part Mating
We evaluate our method on several part-mating tasks with novel objects using a robot equipped with a vision-based tactile sensor.
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control
In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans.
Active Exploration for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years.
Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace.
Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization
Generalizable manipulation requires that robots be able to interact with novel objects and environment.
Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation
In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics.
Imitation and Supervised Learning of Compliance for Robotic Assembly
This can be used to learn a suitable representation of the skill that can be generalized to novel positions of one of the parts involved in the assembly, for example the hole in a peg-in-hole (PiH) insertion task.
PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance
PYROBOCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs).
Markov Modeling of Time-Series Data using Symbolic Analysis
On the other hand, memory estimation of the symbolic sequence helps to extract the predictive patterns in the discretized data.
Training Larger Networks for Deep Reinforcement Learning
Previous work has shown that this is mostly due to instability during training of deep RL agents when using larger networks.
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks
One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in handling the uncertainty in the location of the target hole.
CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context
The purpose of these agents is to quickly adapt and/or generalize their notion of physics of interaction in the real world based on certain features about the interacting objects that provide different contexts to the predictive models.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency.
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR).
Multi-label Prediction in Time Series Data using Deep Neural Networks
In the most general setting of these types of problems, one or more samples of data across multiple time series can be assigned several concurrent fault labels from a finite, known set and the task is to predict the possibility of fault occurrence over a desired time horizon.
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem.
Model-based Reinforcement Learning
reinforcement-learning
+2
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics.
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function
To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves: (i) as a merit function, vanishing only at the first-order stationary points of each player's optimization problem, and (ii) provides error bounds to a stationary Nash point.
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.
Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data
In the second example, the data set is taken from NASA's data repository for prognostics of bearings on rotating shafts.
