ForceSight: Robust Mobile Manipulation Guided by Visual-Force Goals and Natural Language

By Aryaman PattnayakReviewed by Susha Cheriyedath, M.Sc.Sep 26 2023

In a study submitted to the arxiv* server, researchers from Georgia Tech have developed ForceSight. This system utilizes visual-force goals predicted by a deep neural network to execute robust mobile manipulation tasks guided by natural language instructions.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.

Enabling robots to perform practical tasks in human environments guided by high-level instructions remains an open challenge. While prior methods have used language and vision for manipulation, accurately modeling contact forces is also critical.

The researchers propose ForceSight, which predicts visual-force goals from an input image and text prompt. These goals combine a desired end-effector pose and grip forces. Experiments indicate such force representations significantly improve performance over vision alone.

ForceSight utilizes a vision transformer architecture adapted for Red Green Blue-Depth (RGBD) input at its core. It encodes the image patches along with a text embedding from the prompt. The resulting visual features are decoded into an affordance map predicting suitable end-effector locations and other goal parameters.

A visual-force goal comprises a kinematic goal specifying the 3D gripper pose and a force goal with target grip and applied forces. The text prompt is appended with action primitives like 'grasp' and 'lift' to provide context. These goals represent subtasks toward completing the overall instruction.

ForceSight was trained on a dataset of RGBD images paired with ground truth visual-force goals collected by teleoperating a mobile manipulator. Data augmentation and noise injection during collection enhanced model robustness.

Experimental Setup and Results

The researchers evaluated ForceSight on a Stretch RE1 robot performing ten manipulation tasks in unseen real-world environments with novel object instances. It achieved an 81% success rate over 100 trials across pick-and-place, drawer opening, and light switch flipping tasks.

In controlled tests, directly ablating force goals significantly reduced success rates from 90% to 45%, demonstrating their impact. ForceSight outperformed Perceiver-Actor, a prior method adapted for this task, on offline goal prediction metrics. Additional experiments revealed the benefits of depth input, data augmentation, and text conditioning for generalization. The intuitive visual-force goals enable seamless integration with downstream policies.

While promising, ForceSight has some limitations. Depth prediction inaccuracies occasionally persisted for faraway goals. The model also requires targets to be in view, constraining the tasks. Representing gripper orientation was simplified by assuming constant pitch and roll.

The study evaluated a limited set of tabletop tasks with a single robot platform. However, the scalability of vision transformers and efficient data collection methodology suggest the potential for expanding to broader skills. Testing on diverse robots would further establish versatility.

Broader Impact

The ForceSight work demonstrates how combining visual perception with intrinsic force sensing can enhance robotic manipulation. The model outputs intuitive subgoals as visual-force targets valid for downstream controllers. The method also offers a means to incorporate natural language guidance into robots deployed in human spaces. However, further research is needed to expand capabilities and address perception, generalization, and automated task planning limitations.

Challenges and Limitations

Safety protocols remain paramount when deploying such systems around people. The research contributes vital building blocks toward more capable assistive robotics that understand verbal instructions and environmental context. The authors note emergent behaviors like ForceSight applying prompts to suitable but unseen objects, demonstrating generalization across object instances of the same category.

Using Contrastive Language-Image Pretraining (CLIP) or other contrastive models that align visual and textual modalities could further improve the generalization capabilities of the framework. Exploring other modalities beyond vision and touch could promote versatility across diverse environments and tasks. For example, incorporating audio could aid disambiguation and enhance robustness.

Future Outlook

The experiments highlight how directly modeling contact forces can significantly enhance success rates on tasks requiring nuanced tactile interactions like precision grasping. This underscores the importance of multimodal perception and goals for dexterous manipulation. However, the ForceSight model represents just an initial step. The authors outline plans to continue expanding the capabilities and generality of the approach in multiple dimensions.

One key direction is integrating ForceSight with large language models (LLMs) that decompose high-level natural language instructions into sequential subtasks. LLMs have shown promise for long-horizon robotic planning but need a more grounded physical context. The intuitive visual-force goals predicted by ForceSight could provide such helpful grounding.

This integration would enable executing more complex multi-step instructions by breaking them into manageable subgoals and chaining their execution. Handling compositional tasks and language commands remains an open challenge for robotic manipulation systems. Another critical avenue for future work is evaluating the versatility of ForceSight across more diverse robots, environments, and tasks. The current experiments focused on tabletop pick-and-place tasks using a single mobile manipulator platform.

Testing on a wider variety of robotic morphologies and actuation mechanisms will be essential for establishing the generality of the approach. The researchers will also look to expand the diversity of manipulation skills beyond the limited scope. Ensuring reliable performance across the messy complexity of natural human environments will require extensive adaptations. Different settings like kitchens, factories, hospitals, and homes impose unique perceptual, mobility, and manipulation challenges that must be addressed.

However, the scalability of vision transformer backbones, efficient data collection, and modular goal representations demonstrate ForceSight's promising adaptability. With sufficient research and engineering, visual-force-guided manipulation could prove a practical paradigm for assistive robotics.

Understanding natural language commands grounded in physical context and tactile experience will be critical to smooth and safe human-robot collaboration. The ForceSight work offers a pioneering step towards this grand vision, but much remains to be done in transforming proof-of-concept research into capable and dependable real-world robots.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.