3 citations · 10 across the 14 of their papers we have counts for
15 papers
OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation
Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi +3
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot dama…
Learning Causal Structure Distributions for Robust Planning
Alejandro Murillo-Gonzalez, Junhong Xu, Lantao Liu
Structural causal models describe how the components of a robotic system interact. They provide both structural and functional information about the relationships that are present…
ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
Youwei Yu, Junhong Xu, Lantao Liu
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured environments.…
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation
Youwei Yu, Junhong Xu, Lantao Liu
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains. The…
Context-Generative Default Policy for Bounded Rational Agent
Durgakant Pushp, Junhong Xu, Zheng Chen +1
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the default policy,' based on previous…
Boundary-Aware Value Function Generation for Safe Stochastic Motion Planning
Junhong Xu, Kai Yin, Jason M. Gregory +2
Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that…
Coordination of Bounded Rational Drones through Informed Prior Policy
Durgakant Pushp, Junhong Xu, Lantao Liu
Biological agents, such as humans and animals, are capable of making decisions out of a very large number of choices in a limited time. They can do so because they use their prior…
Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy
Ihab S. Mohamed, Junhong Xu, Gaurav S Sukhatme +1
The classical Model Predictive Path Integral (MPPI) control framework, while effective in many applications, lacks reliable safety features due to its reliance on a risk-neutral tr…
Causal Inference for De-biasing Motion Estimation from Robotic Observational Data
Junhong Xu, Kai Yin, Jason M. Gregory +1
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning f…
Decision-Making Among Bounded Rational Agents
Junhong Xu, Durgakant Pushp, Kai Yin +1
When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while…
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Junhong Xu, Kai Yin, Zheng Chen +3
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructur…
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
Junhong Xu, Kai Yin, Lantao Liu
We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the fu…
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes
Junhong Xu, Kai Yin, Lantao Liu
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning fram…
Reachable Space Characterization of Markov Decision Processes with Time Variability
Junhong Xu, Kai Yin, Lantao Liu
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in u…
State-Continuity Approximation of Markov Decision Processes via Finite Element Methods for Autonomous System Planning
Junhong Xu, Kai Yin, Lantao Liu
Motion planning under uncertainty for an autonomous system can be formulated as a Markov Decision Process with a continuous state space. In this paper, we propose a novel solution…