RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation (2407.04689v1)

Abstract: This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, dubbed RAM, featuring generalizability across various objects, environments, and embodiments. Unlike existing approaches that learn manipulation from expensive in-domain demonstrations, RAM capitalizes on a retrieval-based affordance transfer paradigm to acquire versatile manipulation capabilities from abundant out-of-domain data. First, RAM extracts unified affordance at scale from diverse sources of demonstrations including robotic data, human-object interaction (HOI) data, and custom data to construct a comprehensive affordance memory. Then given a language instruction, RAM hierarchically retrieves the most similar demonstration from the affordance memory and transfers such out-of-domain 2D affordance to in-domain 3D executable affordance in a zero-shot and embodiment-agnostic manner. Extensive simulation and real-world evaluations demonstrate that our RAM consistently outperforms existing works in diverse daily tasks. Additionally, RAM shows significant potential for downstream applications such as automatic and efficient data collection, one-shot visual imitation, and LLM/VLM-integrated long-horizon manipulation. For more details, please check our website at https://yxkryptonite.github.io/RAM/.

Retrieval-Based Affordance Transfer for Zero-Shot Robotic Manipulation

The paper presents RAM, a novel methodology for zero-shot robotic manipulation, which focuses on leveraging retrieval-based affordance transfer to enable robots to perform diverse manipulation tasks without prior expert demonstrations in the target domain. By harnessing rich affordance insights from various out-of-domain data sources, RAM demonstrates significant strides in cross-object, cross-domain, and cross-embodiment generalization beyond traditional robot manipulation strategies.

The RAM architecture is distinct by its two-fold approach. Initially, it constructs a comprehensive affordance memory by extracting 2D affordance representations from diverse datasets, such as robotic data, hand-object interaction data, and custom-annotated data. This memory encompasses a wide array of actionable knowledge that spans various objects and environments. Subsequently, RAM employs a hierarchical retrieval process to identify the most relevant demonstration from this affordance memory based on a given language instruction. This approach ensures an optimal selection using semantic and geometrical filtering to closely match the target task and environment.

The primary innovation is RAM's ability to transfer these 2D affordances into 3D executable formats. Through a sampling-based affordance lifting module, 2D affordance is transformed into a 3D representation, encapsulating the contact point and post-contact trajectory, facilitating direct execution by robotic platforms. This lifting approach is executed effectively with off-the-shelf grasp generators and motion planners, bridging the gap between perception and action in robotic systems.

Key Results and Analysis

RAM has shown superior performance compared to baseline methods such as Where2Act, VRB, and Robo-ABC. It consistently yields higher success rates across a variety of tasks, as evidenced in both simulated environments and real-world scenarios. Notably, its numerical success in a controlled setting, achieving a 52.62% average success rate, underscores its potency relative to strategies dependent largely on in-domain learning. Moreover, detailed experiments reveal RAM's embodiment-agnostic nature, supported by evaluations on diverse robotic platforms, further validating its generalizable application.

Beyond immediate performance metrics, RAM's structure also holds substantial promise for adjacent domains in robotics. RAM's capability of autonomously collecting high-quality demonstration data for policy learning speaks to its utility in automating data collection and refining robotic learning models. Additionally, RAM's architecture facilitates seamless integration with LLMs and VLMs for comprehensive task execution, allowing for complex, long-horizon task completions guided by sequence decomposition and instruction understanding.

Future Implications and Limitations

The introduction of RAM into zero-shot robotic manipulation serves both as a practical tool for immediate implementation and a theoretical framework that may inspire further exploration into cross-domain learning approaches. The ability to efficiently employ visual foundation models like Stable Diffusion and DINOv2 for feature extraction highlights the expanding synergy between LLMs and robotics. Furthermore, the promise of leveraging such models to interpret affordances at scale may foster advancements in complex task scheduling and robotics infrastructure development.

However, RAM is not without limitations. The chaining of multiple steps for long-horizon task execution, although functional, could benefit from more holistic planning processes akin to meta-reasoning systems. Furthermore, RAM's current limitations in performing inherently complex manipulations such as dynamic object interactions or intricacies like screwing suggest areas for enhancement. Optimizing affordance transfer methods to intuitively map 2D to 3D trajectories will be essential for accommodating sophisticated manipulation tasks.

In conclusion, RAM provides a robust, scalable framework for zero-shot robotic manipulation, with substantial contributions to generalization methodologies and potential for broader adoption in robotics research—especially in fields seeking to diminish the reliance on bespoke, in-domain demonstrations. Future work on integrating more advanced planning models and enhancing the detailed action translation of affordances could further propel RAM's capabilities, addressing both current challenges and the burgeoning needs of autonomous robotics.