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Found 22 papers, 6 papers with code
Constrained Stein Variational Trajectory Optimization
We present Constrained Stein Variational Trajectory Optimization (CSVTO), an algorithm for performing trajectory optimization with constraints on a set of trajectories in parallel.
Integrated Object Deformation and Contact Patch Estimation from Visuo-Tactile Feedback
Representing the object geometry and contact with the environment implicitly allows a single model to predict contact patches of varying complexity.
CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data
Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data.
Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification
We evaluate our approach on three existing LTL/natural language datasets and show that we can translate natural language commands at 75\% accuracy with far less human data ($\le$12 annotations).
Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory
We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (e. g., RGB-D images) and a learned perception module in the feedback control loop.
Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection
We propose a Model Predictive Control (MPC) method for collision-free navigation that uses amortized variational inference to approximate the distribution of optimal control sequences by training a normalizing flow conditioned on the start, goal and environment.
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations.
Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems
We derive a trajectory tracking error bound for a contraction-based controller that is subjected to this model error, and then learn a controller that optimizes this tracking bound.
Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation
In this work, we propose Multihypothesis Segmentation Tracking (MST), a novel method for volumetric segmentation in changing scenes, which allows scene ambiguity to be tracked and our estimates to be adjusted over time as we interact with the scene.
Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models
We use these images to train a perception model that estimates the simple model state from observations of the complex system online.
Diverse Plausible Shape Completions from Ambiguous Depth Images
We propose PSSNet, a network architecture for generating diverse plausible 3D reconstructions from a single 2. 5D depth image.
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
We present a method for learning to satisfy uncertain constraints from demonstrations.
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
In order to manipulate a deformable object, such as rope or cloth, in unstructured environments, robots need a way to estimate its current shape.
TAMPC: A Controller for Escaping Traps in Novel Environments
We propose an approach to online model adaptation and control in the challenging case of hybrid and discontinuous dynamics where actions may lead to difficult-to-escape "trap" states, under a given controller.
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Our method imposes the feedback law existence as a constraint in a sampling-based planner, which returns a feedback policy around a nominal plan ensuring that, if the Lipschitz constant estimate is valid, the true system is safe during plan execution, reaches the goal, and is ultimately invariant in a small set about the goal.
Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations
We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula.
Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty
We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner.
Learning Parametric Constraints in High Dimensions from Demonstrations
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations.
Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments.
Learning Constraints from Demonstrations
We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations.
Bandit-Based Model Selection for Deformable Object Manipulation
The key contribution of this paper is to formulate the task as a Multi-Armed Bandit problem, with each arm representing a model of the deformable object.
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
