52 citations · 66 across the 11 of their papers we have counts for
13 papers
TerraTransfer++: Demonstration-Free End-to-End Planner
Zikang Xiong, Weixin Li, Zhouchonghao Wu +6
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all exi…
Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion
Joe Eappen, Zikang Xiong, Shreyash S. Iyengar +1
Multi-agent systems in the real-world (e.g., drone swarms, autonomous cars, warehouse robots) must satisfy rich, temporal tasks while avoiding collisions. Signal Temporal Logic (ST…
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
Zikang Xiong, Weixin Li, Zhouchonghao Wu +6
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all sta…
A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems
Zikang Xiong, Joe Kurian Eappen, Suresh Jagannathan
In autonomous driving systems, motion planning is commonly implemented as a two-stage process: first, a trajectory proposer generates multiple candidate trajectories, then a scorin…
Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications
Joe Eappen, Zikang Xiong, Dipam Patel +2
Existing methods for safe multi-agent control using logic specifications like Signal Temporal Logic (STL) often face scalability issues. This is because they rely either on single-…
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
Yi Wu, Zikang Xiong, Yiran Hu +5
Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adher…
Manipulating Neural Path Planners via Slight Perturbations
Zikang Xiong, Suresh Jagannathan
Data-driven neural path planners are attracting increasing interest in the robotics community. However, their neural network components typically come as black boxes, obscuring the…
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Zikang Xiong, Daniel Lawson, Joe Eappen +2
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamic…
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
Zikang Xiong, Joe Eappen, He Zhu +1
Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as p…
Model-free Neural Lyapunov Control for Safe Robot Navigation
Zikang Xiong, Joe Eappen, Ahmed H. Qureshi +1
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks. While a model-free DRL algorithm can s…
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning
Zikang Xiong, Suresh Jagannathan
There has been significant recent interest in devising verification techniques for learning-enabled controllers (LECs) that manage safety-critical systems. Given the opacity and la…
Robustness to Adversarial Attacks in Learning-Enabled Controllers
Zikang Xiong, Joe Eappen, He Zhu +1
Learning-enabled controllers used in cyber-physical systems (CPS) are known to be susceptible to adversarial attacks. Such attacks manifest as perturbations to the states generated…
An Inductive Synthesis Framework for Verifiable Reinforcement Learning
He Zhu, Zikang Xiong, Stephen Magill +1
Despite the tremendous advances that have been made in the last decade on developing useful machine-learning applications, their wider adoption has been hindered by the lack of str…