Publications de Yuejiang Liu | EPFL Graph Search

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Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under distribution shifts and inefficient for knowledge transfer. In this work, we pro ...

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

We propose TAROT, a Targeted data selection framework grounded in Optimal Transport theory. Previous targeted data selection methods primarily use influencebased greedy heuristics to enhance domain-specific performance. These methods perform well on limite ...

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting p ...

Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not eff ...

Yuejiang Liu

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Learning Robust and Adaptive Representations: from Interactions, for Interactions

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective

Collaborative Sampling in Generative Adversarial Networks

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

TAROT: Targeted Data Selection via Optimal Transport

Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Learning Decoupled Representations for Human Pose Forecasting

On Pitfalls of Test-time Adaptation