103 citations · 322 across the 32 of their papers we have counts for
34 papers
Generalizable Robotic Insertion with World Models
Nicklas Hansen, Iretiayo Akinola, Yijie Guo +7
Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies specialized to each insertion…
FACT: Failure-Aware Causal Training for World-Action Models
Quanquan Peng, Yutong Liang, Rui Yan +2
Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on the future-prediction ability…
RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
Hanan Gani, Tejal Kulkarni, Madhoolika Chodavarapu +2
Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional.…
Hallucination in World Models is Predictable and Preventable
Nicklas Hansen, Xiaolong Wang
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the g…
Generating Robot Hands from Human Demonstrations
Sha Yi, Nicklas Hansen, Xueqian Bai +3
Robot learning has advanced rapidly in learning control, but learning the physical body of a robot remains much more difficult because jointly searching over design and control cre…
Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Darrien McKenzie, Nicklas Hansen, Xiaolong Wang
Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems…
Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations
Minghao Fu, Fan Feng, Nicklas Hansen +1
World models enable agents to predict future dynamics conditioned on actions, making the choice of latent representation central to planning and control. Such representations are o…
TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs
Yutao Xie, Nathaniel Thomas, Nicklas Hansen +3
Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but training still remai…
Learning Massively Multitask World Models for Continuous Control
Nicklas Hansen, Hao Su, Xiaolong Wang
General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-ta…
Learning to Design Soft Hands using Reward Models
Xueqian Bai, Nicklas Hansen, Adabhav Singh +5
Soft robotic hands promise to provide compliant and safe interaction with objects and environments. However, designing soft hands to be both compliant and functional across diverse…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Adrià López Escoriza, Nicklas Hansen, Stone Tao +2
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively e…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Adrian Remonda, Nicklas Hansen, Ayoub Raji +4
Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars opera…
PWM: Policy Learning with Multi-Task World Models
Ignat Georgiev, Varun Giridhar, Nicklas Hansen +1
Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World model methods offer scalability by…
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S, Vlad Sobal +3
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
Abdulaziz Almuzairee, Nicklas Hansen, Henrik I. Christensen
Q-learning algorithms are appealing for real-world applications due to their data-efficiency, but they are very prone to overfitting and training instabilities when trained from vi…
Finetuning Offline World Models in the Real World
Yunhai Feng, Nicklas Hansen, Ziyan Xiong +2
Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency…
TD-MPC2: Scalable, Robust World Models for Continuous Control
Nicklas Hansen, Hao Su, Xiaolong Wang
TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In t…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran +1
Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminat…
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
Yanjie Ze, Ge Yan, Yueh-Hua Wu +6
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To ach…
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Nicklas Hansen, Yixin Lin, Hao Su +3
Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-…
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Nicklas Hansen, Zhecheng Yuan, Yanjie Ze +5
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks. We revisit a simple Learning-from-Scratch (LfS) baseline that incorporates data augmentat…