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Amir Rasouli

20 accepted papers

2026

CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance

Rui Heng Yang, Xuan Zhao, Léo Maxime Brunswic, Montgomery Tucker Alban, Matéo Clémente, Tongtong Cao +2
ICRA 2026poster

In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal performance following this regiment requires a large-scale dataset, which is costly to obtain, especially for challenging ta…

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2026

Distracted Robot: How Visual Clutter Undermine Robotic Manipulation

Amir Rasouli, Montgomery Tucker Alban, Sajjad Pakdamansavoji, Zhiyuan Li, Zhanguang Zhang, Yangzheng Wu +1
ICRA 2026poster

In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation from a psychophysical perspective, therefore we use a unified measure of clutter that accounts for environmental factors…

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2026

HIPPo: Harnessing Image-To-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
ICRA 2026poster

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is…

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2026

Improving Robotic Manipulation Robustness Via NICE Scene Surgery

Sajjad Pakdamansavoji, Mozhgan Pourkeshavarz, Adam Sigal, Zhiyuan Li, Rui Heng Yang, Amir Rasouli
ICRA 2026poster

Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety. In this work, we propose an effective and scalable framework, Naturalistic Inpainting for Context Enhancement (NICE). Ou…

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2025

Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving

Ehsan Ahmadi, Ray Coden Mercurius, Soheil Alizadeh, Kasra Rezaee, Amir Rasouli
ICRA 2025

Trajectory prediction models in autonomous driving are vulnerable to perturbations from non-causal agents whose actions should not affect the ego-agent's behavior. Such perturbations can lead to incorrect predictions of other agents' trajectories, potentially compromising the safety and efficiency o

Cited by 7Source ↗Scholar ↗
2025

Ergodic Generative Flows

Leo Maxime Brunswic, Mateo Clémente, Rui Heng Yang, Adam Sigal, Amir Rasouli, Yinchuan Li
ICML 2025poster

Generative Flow Networks (GFNs) were initially introduced on directed non-acyclic graphs to sample from an unnormalized distribution density. Recent works have extended the theoretical framework for generative methods allowing more flexibility and enhancing application range. However, many challenge…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
RA-L 2025

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is

Cited by 4Source ↗Scholar ↗
2025

RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning

Xi Ye, Rui Heng Yang, Jun Jin, Yinchuan Li, Amir Rasouli
IROS 2025

Diffusion models exhibit impressive scalability in robotic task learning, yet they struggle to adapt to novel, highly dynamic environments. This limitation primarily stems from their constrained replanning ability: they either operate at a low frequency due to a time-consuming iterative sampling pro

Cited by 5Source ↗Scholar ↗
2025

Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising

Mateo Clémente, Leo Maxime Brunswic, Rui Heng Yang, Xuan Zhao, Yasser H. Khalil, Haoyu LEI +2
NeurIPS 2025poster

Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly tran…

Cited by 0Source ↗Scholar ↗
2024

A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction

Amir Rasouli
ICRA 2024poster

In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction models. In this regard, we also prop…

Cited by 9Source ↗Scholar ↗
2024

Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving

Mozhgan Pourkeshavarz, Mohammad Sabokrou, Amir Rasouli
CVPR 2024poster

In autonomous driving behavior prediction is fundamental for safe motion planning hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying…

Cited by 10Source ↗PDF ↗Scholar ↗
2024

CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving

Changhe Chen, Mozhgan Pourkeshavarz, Amir Rasouli
ICRA 2024poster

Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scenarios, such as cruising, and distance-based metrics that are computed by averaging over all scenarios. Following such a r…

Cited by 3Source ↗code ↗Scholar ↗
2024

CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving

Mozhgan Pourkeshavarz, Junrui Zhang, Amir Rasouli
CVPR 2024poster

For safe motion planning in real-world autonomous vehicles require behavior prediction models that are reliable and robust to distribution shifts. The recent studies suggest that the existing learning-based trajectory prediction models do not posses such characteristics and are susceptible to small…

Cited by 10Source ↗PDF ↗Scholar ↗
2024

DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction

Rezaul Karim, Soheil Mohamad Alizadeh Shabestary, Amir Rasouli
ICRA 2024poster

Predicting temporally consistent road users’ trajectories in a multi-agent setting is a challenging task due to the unknown characteristics of agents and their varying intentions. Besides using semantic map information and modeling interactions, it is important to build an effective mechanism capabl…

Cited by 7Source ↗Scholar ↗
2024

DySeT: a Dynamic Masked Self-distillation Approach for Robust Trajectory Prediction

Mozghan Pourkeshavarz*, Arielle Zhang, Amir Rasouli
ECCV 2024poster

"The lack of generalization capability of behavior prediction models for autonomous vehicles is a crucial concern for safe motion planning. One way to address this is via self-supervised pre-training through masked trajectory prediction. However, the existing models rely on uniform random sampling o…

Cited by 5Source ↗PDF ↗Scholar ↗
2023

Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction

Mozhgan Pourkeshavarz, Changhe Chen, Amir Rasouli
ICCV 2023poster

Predicting diverse yet admissible trajectories that adhere to the map constraints is challenging. Graph-based scene encoders have been proven effective for preserving local structures of maps by defining lane-level connections. However, such encoders do not capture more complex patterns emerging fro…

Cited by 16PDF ↗Scholar ↗
2023

PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning

Amir Rasouli, Iuliia Kotseruba
ICRA 2023poster

Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that could impact the way pedestrians behave. To address this challenge, we propose a novel framework that relies on different data modal…

Cited by 44Source ↗Scholar ↗
2021

Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction

Tiffany Yau, Saber Malekmohammadi, Amir Rasouli, Peter Lakner, Mohsen Rohani, Jun Luo
ICRA 2021poster

One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossing. Predicting behaviour depends on many social and environmental factors, particularly interactions between road users.…

Cited by 27Source ↗code ↗Scholar ↗
2019

PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction

Amir Rasouli, Iuliia Kotseruba, Toni Kunic, John K. Tsotsos
ICCV 2019oral

Pedestrian behavior anticipation is a key challenge in the design of assistive and autonomous driving systems suitable for urban environments. An intelligent system should be able to understand the intentions or underlying motives of pedestrians and to predict their forthcoming actions. To date, onl…

Cited by 469PDF ↗Scholar ↗