← Search

Bingqing Chen

10 accepted papers

2026

LightTact: A Visual-Tactile Fingertip Sensor for Deformation-Independent Contact Sensing

RSS 2026poster

Contact often occurs without macroscopic surface deformation, such as during interaction with liquids, semi-liquids, or ultra-soft materials. However, most existing tactile sensors rely on deformation to infer contact, making such light-contact interactions difficult to perceive robustly. To address…

Cited by 0SourceScholar
2025

CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios Using Real-World Trajectories

ICRA 2025

Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing. An outstanding challenge with simulation-based testing is the generation of safety-critical scenarios, which are essential to ensure that AVs c

Cited by 11SourceScholar
2025

GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering

CoRL 2025poster

In Embodied Question Answering (EQA), agents must explore and develop a semantic understanding of an unseen environment in order to answer a situated question with confidence. This remains a challenging problem in robotics, due to the difficulties in obtaining useful semantic representations, updati…

Cited by 0SourcecodeScholar
2025

Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining

RSS 2025poster

Quadrupedal robots have demonstrated impressive locomotion capabilities in complex environments, but equipping them with autonomous versatile manipulation skills in a scalable way remains a significant challenge. In this work, we introduce a system that integrates data collection and imitation learn…

Cited by 0PDFcodeScholar
2025

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

ICLR 2025poster

Robot learning is witnessing a significant increase in the size, diversity, and complexity of pre-collected datasets, mirroring trends in domains such as natural language processing and computer vision. Many robot learning methods treat such datasets as multi-task expert data and learn a multi-task,…

Cited by 1SourcePDFScholar
2024

From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers

CVPR 2024poster

Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted least squares optimization problem to solve for the poses. Recent work has shown that coupling the two problems by iterati…

2023

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery

CoRL 2023poster

Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynamics}, yielding inaccuracies that manifest as the dynamical si…

Cited by 33SourceScholar
2021

Learn-To-Race: A Multimodal Control Environment for Autonomous Racing

ICCV 2021poster

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, ve…

Cited by 40PDFcodeScholar
2020

Damage-Sensitive and Domain-Invariant Feature Extraction for Vehicle-Vibration-Based Bridge Health Monitoring

ICASSP 2020accepted

We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health. Motivated by indirect sensing methods' benefits, such as low-cost and low-mainte…

Cited by 0SourceScholar
2020

Dyna-Bolt: Domain Adaptive Binary Factorization Of Current Waveforms For Energy Disaggregation

ICASSP 2020accepted

Non-intrusive load monitoring (NILM) is the set of algorithmic techniques for inferring the operational states of individual appliances in a household given the aggregate electrical measurements at a single point of instrumentation. Most successful techniques to-date approach the problem from a supe…

Cited by 0SourceScholar