LATTE: LAnguage Trajectory TransformEr (2208.02918v3)

Abstract: Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the real world is far from being an easy task. The challenge of combining a robot's inherent low-level geometric and kinodynamic constraints with a human's high-level semantic instructions traditionally is solved using task-specific solutions with little generalizability between hardware platforms, often with the use of static sets of target actions and commands. This work instead proposes a flexible language-based framework that allows a user to modify generic robotic trajectories. Our method leverages pre-trained LLMs (BERT and CLIP) to encode the user's intent and target objects directly from a free-form text input and scene images, fuses geometrical features generated by a transformer encoder network, and finally outputs trajectories using a transformer decoder, without the need of priors related to the task or robot information. We significantly extend our own previous work presented in Bucker et al. by expanding the trajectory parametrization space to 3D and velocity as opposed to just XY movements. In addition, we now train the model to use actual images of the objects in the scene for context (as opposed to textual descriptions), and we evaluate the system in a diverse set of scenarios beyond manipulation, such as aerial and legged robots. Our simulated and real-life experiments demonstrate that our transformer model can successfully follow human intent, modifying the shape and speed of trajectories within multiple environments. Codebase available at: https://github.com/arthurfenderbucker/LaTTe-Language-Trajectory-TransformEr.git

LATTE: An Analysis of the Language Trajectory Transformer Approach

The paper "LATTE: LAnguage Trajectory TransformEr" presents a novel methodology for integrating natural language processing with robotic trajectory modification. The authors propose a system that translates human intent, expressed in natural language, into meaningful modifications of robotic trajectories using a combination of pre-trained LLMs and a transformer-based architecture.

The central challenge addressed in this work is the gap between high-level semantic instructions provided by humans and the low-level geometric and kinodynamic constraints that govern robotic motion. Traditional approaches in this space have relied heavily on task-specific solutions, which often lack generalizability across different hardware platforms. In contrast, this paper advocates for a more generalizable approach by leveraging the capabilities of established LLMs like BERT and CLIP. These models encode semantic intent and object information from text and scene images, respectively.

The architecture proposed by the authors comprises three main components: a language and image encoder, a geometry encoder, and a multi-modal transformer decoder. The pre-trained BERT and CLIP models generate semantic embeddings from user instructions and object images, while a transformer encoder processes geometric features derived from the original trajectory. The transformer decoder then produces the modified trajectory by autoregressively aligning these semantic and geometric embeddings.

The contributions of this paper extend beyond its novel architecture. It also introduces improvements in trajectory handling, expanding the parameterization space to include 3D and velocity dimensions. This allows for more nuanced and context-aware modifications in trajectory planning. Furthermore, the authors demonstrate the applicability of their framework across different robotic platforms, including manipulators, aerial vehicles, and legged robots, highlighting its versatility.

From an experimental standpoint, the authors provide a comprehensive evaluation of their approach. They employ procedural data generation to train their models, which significantly reduces the need for extensive human annotation. The use of synthetic datasets allows for diverse and challenging testing scenarios, and the results indicate that the model successfully interprets and implements human intent in modifying trajectories. Notable achievements include the successful integration of object images in the trajectory modification process, a feature that adds a layer of realism and applicability to the proposed system.

The implications of this research are significant for the robotics community. By providing a framework that combines natural language processing with trajectory generation, the paper presents a step towards more intuitive human-robot interactions. The integration of multi-modal input data in such a framework is particularly noteworthy and sets the stage for future explorations in dynamic and unstructured environments where robots must interpret and execute complex tasks based on human commands.

For future directions, the authors suggest enhancing the system's ability to handle additional modalities, such as force feedback, and exploring longer-term interactions that involve multiple instructions. Such developments could potentially enable more sophisticated collaboration between humans and robots, expanding the practical applications of this research.

In conclusion, the LATTE approach offers a promising direction for integrating language-based interfaces with robotic control. By leveraging the power of transformers and pre-trained LLMs, this paper paves the way for more seamless and adaptable human-robot interaction systems.