Robotic Visual Instruction

Abstract: Recently, natural language has been the primary medium for human-robot interaction. However, its inherent lack of spatial precision introduces challenges for robotic task definition such as ambiguity and verbosity. Moreover, in some public settings where quiet is required, such as libraries or hospitals, verbal communication with robots is inappropriate. To address these limitations, we introduce the Robotic Visual Instruction (RoVI), a novel paradigm to guide robotic tasks through an object-centric, hand-drawn symbolic representation. RoVI effectively encodes spatial-temporal information into human-interpretable visual instructions through 2D sketches, utilizing arrows, circles, colors, and numbers to direct 3D robotic manipulation. To enable robots to understand RoVI better and generate precise actions based on RoVI, we present Visual Instruction Embodied Workflow (VIEW), a pipeline formulated for RoVI-conditioned policies. This approach leverages Vision-LLMs (VLMs) to interpret RoVI inputs, decode spatial and temporal constraints from 2D pixel space via keypoint extraction, and then transform them into executable 3D action sequences. We additionally curate a specialized dataset of 15K instances to fine-tune small VLMs for edge deployment,enabling them to effectively learn RoVI capabilities. Our approach is rigorously validated across 11 novel tasks in both real and simulated environments, demonstrating significant generalization capability. Notably, VIEW achieves an 87.5% success rate in real-world scenarios involving unseen tasks that feature multi-step actions, with disturbances, and trajectory-following requirements. Project website: https://robotic-visual-instruction.github.io/

Robotic Visual Instruction: A Paradigm Shift in Human-Robot Interaction

The research paper, "Robotic Visual Instruction," presents an innovative approach to human-robot interaction by introducing Robotic Visual Instruction (RoVI). The authors tackle the limitations inherent in using natural language for defining robotic tasks, particularly concerning spatial precision, ambiguity, and verbosity. They propose RoVI as an alternative communication paradigm utilizing hand-drawn symbolic representations that encode spatiotemporal information into intuitive, human-interpretable 2D sketches. This approach offers a more precise method for guiding robotic tasks, especially in environments where verbal communication may not be feasible.

The RoVI paradigm is centered around visual symbols such as arrows, circles, colors, and numbers to direct robotic manipulation in 3D space. These symbols serve as primitives for conveying movement trajectories, target object locations, and temporal sequences. The novel developments within the paper include the Visual Instruction Embodied Workflow (VIEW), a designed pipeline that interprets RoVI inputs and translates them into executable 3D action sequences via Vision-LLMs (VLMs).

To ensure effective deployment and interpretation of RoVI, the authors curated a specialized dataset of 15,000 instances to fine-tune small VLMs for edge devices, empowering them to grasp RoVI conditions sufficiently. This dataset feeds into VIEW, which houses a pipeline that begins with VLMs processing RoVI and observation images to produce task definitions, detailed planning, and executable action functions. It innovatively uses a keypoint module to extract critical spatial and temporal constraints and guides robotic manipulation through precise action execution policies.

The implications of this research are manifold. Practically, using RoVI allows for silent and precise directives, suitable for environments such as libraries or hospitals where noise is minimized. Theoretically, it pushes forward the application of multimodal approaches in human-robot interaction, demonstrating that visual language can be a viable alternative to text-based directives. Notably, VIEW exhibits an 87.5% success rate in real-world scenarios involving unseen, complex tasks indicating its robust generalization capability.

The findings suggest several future research directions. One pertinent avenue involves scaling up the RoVI Book dataset to encompass a broader array of tasks and drawing styles to deepen models' understanding of free-form visual instructions. Additionally, refining VIEW's processing capabilities for edge deployment would bring practical applications of RoVI closer to reality.

In sum, this paper contributes a significant advancement in the domain of human-robot interaction, opening practical and theoretical opportunities for employing visual communication as a streamlined, efficient method for task directives.