Search Results for author:
Found 48 papers, 15 papers with code
DESPOT: Online POMDP Planning with Regularization
We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy.
SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants.
Robotics Multiagent Systems
Particle Filter Networks with Application to Visual Localization
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc.
Particle Filter Recurrent Neural Networks
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data.
QMDP-Net: Deep Learning for Planning under Partial Observability
It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture.
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
The particle filter maintains a belief using learned discriminative update, which is trained end-to-end for decision making.
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning.
Contrastive Variational Reinforcement Learning for Complex Observations
This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles complex visual observations in DRL.
MAGIC: Learning Macro-Actions for Online POMDP Planning
The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required.
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information?
GAMMA: A General Agent Motion Model for Autonomous Driving
Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with uncertain driver behaviors, both in real-time.
Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties
This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input.
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accepted benchmark.
Hindsight Trust Region Policy Optimization
We propose \emph{Hindsight Trust Region Policy Optimization}(HTRPO), a new RL algorithm that extends the highly successful TRPO algorithm with \emph{hindsight} to tackle the challenge of sparse rewards.
Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction
The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expression, and identifies a set of candidate objects.
Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning
The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term.
Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction
A core issue for the system is semantic and spatial grounding, which is to infer objects and their spatial relationships from images and natural language expressions.
Factored Contextual Policy Search with Bayesian Optimization
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts".
POMDP-lite for Robust Robot Planning under Uncertainty
We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks.
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i. e. a deep neural network.
Adaptive Stochastic Optimization: From Sets to Paths
Adaptive stochastic optimization optimizes an objective function adaptively under uncertainty.
Monte Carlo Value Iteration with Macro-Actions
The recently introduced Monte Carlo Value Iteration (MCVI) can tackle POMDPs with very large discrete state spaces or continuous state spaces, but its performance degrades when faced with long planning horizons.
Solving the Perspective-2-Point Problem for Flying-Camera Photo Composition
The user, instead of holding a camera in hand and manually searching for a viewpoint, will interact directly with image contents in the viewfinder through simple gestures, and the flying camera will achieve the desired viewpoint through the autonomous flying capability of the drone.
Factored Contextual Policy Search with Bayesian Optimization
Our key observation is that experience can be directly generalized over target contexts.
Monte Carlo Bayesian Reinforcement Learning
Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them.
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.
LeTS-Drive: Driving in a Crowd by Learning from Tree Search
LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.
Robot Capability and Intention in Trust-based Decisions across Tasks
In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions.
PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians
Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.
Robotics
Closing the Planning-Learning Loop with Application to Autonomous Driving
To achieve real-time performance for large-scale planning, this work introduces a new algorithm Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation.
Autonomous Driving
Robotics
Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects
Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception.
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation
We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments.
Ab Initio Particle-based Object Manipulation
Like the model-based analytic approaches to manipulation, the particle representation enables the robot to reason about the object's geometry and dynamics in order to choose suitable manipulation actions.
Deep Visual Navigation under Partial Observability
Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments.
Context-Hierarchy Inverse Reinforcement Learning
To solve these issues, we present Context Hierarchy IRL(CHIRL), a new IRL algorithm that exploits the context to scale up IRL and learn reward functions of complex behaviors.
Receding Horizon Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations.
Partially Observable Markov Decision Processes in Robotics: A Survey
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks.
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement
Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding.
The Planner Optimization Problem: Formulations and Frameworks
Identifying internal parameters for planning is crucial to maximizing the performance of a planner.
On the Effective Horizon of Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning over a given time horizon to compute an approximately optimal policy for a hypothesized reward function and then match this policy with expert demonstrations.
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement
Tidying up a messy table may appear simple for humans, but articulating clear criteria for tidiness is challenging due to the ambiguous nature of common sense reasoning.
Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation
The data-driven approach to robot control has been gathering pace rapidly, yet generalization to unseen task domains remains a critical challenge.
LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model
We propose an open state representation that provides continuous expansion and updating of object attributes from the LLM's inherent capabilities for context understanding and historical action reasoning.
LLMs for Robotic Object Disambiguation
The advantages of pre-trained large language models (LLMs) are apparent in a variety of language processing tasks.
On the Empirical Complexity of Reasoning and Planning in LLMs
We found that if problems can be decomposed into a sequence of reasoning steps and learning to predict the next step has a low sample and computational complexity, explicitly outlining the reasoning chain with all necessary information for predicting the next step may improve performance.
