What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data (2204.06252v2)

Abstract: 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 paper, 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. Codebase and trained models available at http://hulc.cs.uni-freiburg.de

Insights into Language Conditioned Robotic Imitation Learning on Unstructured Data

The paper presents a detailed exploration of the critical challenges and components in learning language-conditioned policies for robotic control using unstructured offline datasets. Central to this investigation is the paper of various architectural and algorithmic strategies to enhance the performance of such systems, specifically focusing on the Hierarchical Universal Language Conditioned Policies (HULC).

Background and Objective

The integration of natural language understanding into robotic control systems has been a longstanding objective in robotics, motivated by the need for intuitive human-robot interaction. Recent advances have leveraged end-to-end learning from visual data but face challenges due to the lack of a universally robust process for design assessment across diverse setups. This paper conducts an extensive evaluation of language-conditioned imitation learning, specifically targeting the need to efficiently acquire and execute a repertoire of skills based on flexible user commands.

Methodological Contributions

The paper introduces several improvements over prior approaches. Key contributions include:

1. Hierarchical Learning Structure: A hierarchical decomposition separates global planning from local policy execution. This involves learning global plans from static camera inputs and executing localized control policies using gripper camera inputs. This approach substantially enhances model robustness and task adaptability.
2. Multimodal Transformer Encoder: The authors propose a novel transformer-based architecture for sequence encoding, providing temporal context and enabling better recognition of abstract behaviors from video sequences. This representation supports more comprehensive planning and control.
3. Discrete Latent Plan Spaces: The utilization of discrete latent spaces, characterized by categorical representations, aligns well with the inherently discrete nature of language. This facilitates improved task and subtask organization within the robot's operational framework.
4. Contrastive Visual-Language Alignment: To address the symbol grounding problem, the paper adopts a contrastive loss function to align video and language representations. This maximizes the relevant association and minimizes ambiguous pairings, leveraging the similarity in visual and linguistic domains.
5. Data and Optimization Techniques: Effective data augmentation practices were established, with stochastic image shifts boosting policy learning performance. Additionally, specific adjustments in the weighting of KL loss components address common problems like posterior collapse in variational encoders.

Results and Evaluation

The authors report state-of-the-art performance on the CALVIN benchmark for language-conditioned, long-horizon robot manipulation tasks. Their model outperforms previous approaches, demonstrating significant improvements in completing sequential tasks specified by natural language:

• Sequential Task Completion: The enhanced model achieves higher rates of task completion across multiple sequential language instructions, indicating stronger long-term planning ability.
• Robustness to Contextual Variability: Evaluations across diverse test environments highlight the model's adaptability to differing initial conditions and tasks not encountered during training.
• Advanced LLM Integration: The use of pre-trained LLMs such as MiniLM-L3-v2 and others trained for sentence-level semantic similarity showed significant impact on performance, underscoring the importance of choosing appropriate language encoders.

Implications and Future Directions

The findings suggest several important implications for the future of language-conditioned robotic systems. The hierarchical learning approach, combined with discrete latent plans and advanced alignment strategies, provides a scalable framework applicable to real-world scenarios. The novel use of contrastive visual-linguistic alignment underscores the potential for improving human-robot interaction fidelity.

Future research could explore further integration of LLMs finetuned with robotic control tasks, domain adaption strategies to enhance inter-environment generalization, and real-time applications of these systems in varied robotics platforms. Such developments promise exciting advances in creating generalist robots capable of performing complex, dynamically specified tasks through natural language.