15 citations · 57 across the 12 of their papers we have counts for
13 papers
Reconstructing Objects in-the-wild for Realistic Sensor Simulation
Ze Yang, Sivabalan Manivasagam, Yun Chen +3
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing.…
UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang +1
LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To…
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen +5
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can…
UniSim: A Neural Closed-Loop Sensor Simulator
Ze Yang, Yun Chen, Jingkang Wang +4
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be col…
Cost-Efficient Online Hyperparameter Optimization
Jingkang Wang, Mengye Ren, Ilija Bogunovic +2
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…
Adversarial Attacks On Multi-Agent Communication
James Tu, Tsunhsuan Wang, Jingkang Wang +3
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributi…
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Jingkang Wang, Ava Pun, James Tu +5
As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes…
Learning to Communicate and Correct Pose Errors
Nicholas Vadivelu, Mengye Ren, James Tu +2
Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous message…
Policy Learning Using Weak Supervision
Jingkang Wang, Hongyi Guo, Zhaowei Zhu +1
Most existing policy learning solutions require the learning agents to receive high-quality supervision signals such as well-designed rewards in reinforcement learning (RL) or high…
Zero-Shot Compositional Policy Learning via Language Grounding
Tianshi Cao, Jingkang Wang, Yining Zhang +1
Despite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans…
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Jingkang Wang, Ruoxi Jia, Gerald Friedland +2
Despite the great success achieved in machine learning (ML), adversarial examples have caused concerns with regards to its trustworthiness: A small perturbation of an input results…
Reinforcement Learning with Perturbed Rewards
Jingkang Wang, Yang Liu, Bo Li
Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in…
Multiple Character Embeddings for Chinese Word Segmentation
Jingkang Wang, Jianing Zhou, Jie Zhou +1
Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neur…