Search Results for author:
Found 37 papers, 15 papers with code
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
The rise of foundation models trained on multiple tasks with impressively large datasets from a variety of fields has led researchers to believe that these models may provide common sense reasoning that existing planners are missing.
Towards Provable Log Density Policy Gradient
In this work, we argue that this residual term is significant and correcting for it could potentially improve sample-complexity of reinforcement learning methods.
D$^3$Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Robotic Manipulation
These fields capture the dynamics of the underlying 3D environment and encode both semantic features and instance masks.
Marginalized Importance Sampling for Off-Environment Policy Evaluation
This paper proposes a new approach to evaluate the real-world performance of agent policies prior to deploying them in the real world.
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding
Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language.
Dynamic-Resolution Model Learning for Object Pile Manipulation
Prior works typically assume representation at a fixed dimension or resolution, which may be inefficient for simple tasks and ineffective for more complicated tasks.
Adaptive Failure Search Using Critical States from Domain Experts
Adaptive Stress Testing (AST) is one such method that poses the problem of failure search as a Markov decision process and uses reinforcement learning techniques to find high probability failures.
Conveying Autonomous Robot Capabilities through Contrasting Behaviour Summaries
In this paper we present an adaptive search method for efficiently generating contrasting behaviour summaries with support for continuous state and action spaces.
Towards Co-operative Congestion Mitigation
The effects of traffic congestion are widespread and are an impedance to everyday life.
Occlusion-Aware Crowd Navigation Using People as Sensors
To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy.
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction
We employ MESRNN for pedestrian trajectory prediction, utilizing these meta-path based features to capture the relationships between the trajectories of pedestrians at different points in time and space.
Off Environment Evaluation Using Convex Risk Minimization
We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning.
Learning Visual-Audio Representations for Voice-Controlled Robots
Previous methods rely on a large number of labels and task-specific reward functions.
Multi-Agent Variational Occlusion Inference Using People as Sensors
We propose an occlusion inference method that characterizes observed behaviors of human agents as sensor measurements, and fuses them with those from a standard sensor suite.
Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction
Multi-pedestrian trajectory prediction is an indispensable element of autonomous systems that safely interact with crowds in unstructured environments.
Building Mental Models through Preview of Autopilot Behaviors
Effective human-vehicle collaboration requires an appropriate un-derstanding of vehicle behavior for safety and trust.
Improving the Feasibility of Moment-Based Safety Analysis for Stochastic Dynamics
We then reformulate the constraints of the optimization to mitigate the computational limitations associated with an increase in state dimensionality.
Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments
To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision.
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Safe and efficient navigation through human crowds is an essential capability for mobile robots.
Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty
Robust design of autonomous systems under uncertainty is an important yet challenging problem.
Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians.
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
We present a review and taxonomy of 200 models from the literature on driver behavior modeling.
Monte-Carlo Tree Search for Policy Optimization
Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points.
Online monitoring for safe pedestrian-vehicle interactions
As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated.
Robotics Multiagent Systems Signal Processing
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension.
Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation
Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems.
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning.
Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles.
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers.
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment.
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors.
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors.
