What Matters in Language Conditioned Robotic Imitation Learning Over Unstructured Data

By Oier Mees, Lukás Hermann, Wolfram Burgard. 2022

209 citations recorded by the source corpus.

A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this letter, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Reinforcement Learning

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Citations in the corpus

Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Various works have investigated strategies for the challenging problem of fusing vision, language, and control [60, 61, 62, 63, 64, 65, 66].”
Visual Language Maps for Robot Navigation (2022, background): “The recent success of large pretrained vision and language models [10], [27] has spurred a flurry of interest in applying their zero-sot capabilities to different domains including object detection and segmentation [28], [29], [11], robot manipulation [30], [31], [32], [33], and navigation [13], [12”
RVT: Robotic View Transformer for 3D Object Manipulation (2023, background): “With the recent breakthrough in large language models, multi-task robot learning has been approached by using natural language to specify a broad range of tasks and learning the policy from large pre-collected datasets [35, 26, 36, 2, 37, 38, 39, 40, 41].”
Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models (2023, methodology): “This includes methods that explicitly tackle long-horizon language-based control on CALVIN such as multi-context imitation (MCIL) [41], hierarchical universal language-conditioned policy (HULC) [44], and improved variants of HULC [18].”
Octo: An Open-Source Generalist Robot Policy (2024, methodology): “Model architecture: Prior transformer-based policy designs typically encode input images with large ResNet-style [32] encoders and fuse the resulting image features with a comparatively small transformer [10, 67, 81, 17, 98, 61, 83].”
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation (2023, methodology): “We compare with four baseline methods: MCIL (Lynch & Sermanet, 2020), RT-1 (Brohan et al., 2022), HULC (Mees et al., 2022b), and a multi-task version of R3M (Nair et al., 2022).”
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022, background): “Recently, a number of end-to-end approaches [13, 12, 67, 68, 69] have been proposed for conditioning BC agents with language instructions.”
Vision-Language Foundation Models as Effective Robot Imitators (2023, methodology): “HULC (Mees et al., 2022a) utilizes the vision encoder of Lynch & Sermanet (2020) trained on the CALVIN dataset (Mees et al., 2022b) and some pre-trained language encoder models such as sentence transformer (Reimers & Gurevych, 2019), and their HULC++ (Mees et al., 2023) also fine-tunes these…”
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies (2023, methodology): “Prior work aimed to extract representations from play data for effective downstream policy learning [47] or learned self-supervised representations of skills, referred to as latent plans, using Conditional Variational Autoencoders (CVAE) [12, 21, 25, 24].”
A Survey on Vision--Language--Action Models for Embodied AI (2024, unknown): “HULC [96] introduces several techniques aimed at enhancing robot learning architectures.”
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models (2022, methodology): “Motivated by promising results of CLIP in robotics in prior works [35, 42], our instantiation of DIAL uses CLIP [39] for both instruction augmentation and task representation; nonetheless, other VLMs or captioning models could also be used to propose instruction augmentations.”
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023, unknown): “The project website is robotics-transformer-x.github.io.”
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023, background): “…to novel object instances [31, 34, 32, 35, 36], to tasks involving novel combinations of objects and skills [37, 38, 39, 40, 41], to new goals or language instructions [42, 43, 41, 44, 45, 46, 47], to tasks with novel semantic object categories [48, 49], and to unseen environments [50, 51, 52].”