ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations (2107.14483v5)

Abstract: Object manipulation from 3D visual inputs poses many challenges on building generalizable perception and policy models. However, 3D assets in existing benchmarks mostly lack the diversity of 3D shapes that align with real-world intra-class complexity in topology and geometry. Here we propose SAPIEN Manipulation Skill Benchmark (ManiSkill) to benchmark manipulation skills over diverse objects in a full-physics simulator. 3D assets in ManiSkill include large intra-class topological and geometric variations. Tasks are carefully chosen to cover distinct types of manipulation challenges. Latest progress in 3D vision also makes us believe that we should customize the benchmark so that the challenge is inviting to researchers working on 3D deep learning. To this end, we simulate a moving panoramic camera that returns ego-centric point clouds or RGB-D images. In addition, we would like ManiSkill to serve a broad set of researchers interested in manipulation research. Besides supporting the learning of policies from interactions, we also support learning-from-demonstrations (LfD) methods, by providing a large number of high-quality demonstrations (~36,000 successful trajectories, ~1.5M point cloud/RGB-D frames in total). We provide baselines using 3D deep learning and LfD algorithms. All code of our benchmark (simulator, environment, SDK, and baselines) is open-sourced, and a challenge facing interdisciplinary researchers will be held based on the benchmark.

• The paper presents ManiSkill, a benchmark that assesses generalizable manipulation skills using diverse 3D objects and simulation tasks.
• It leverages an extensive dataset of approximately 36,000 trajectories and 1.5 million frames to support learning-from-demonstrations.
• Experimental results reveal imitation learning limitations, underscoring the need for enhanced 3D perception and adaptive policy algorithms.

ManiSkill: A Benchmark for Object Manipulation Skills

The paper "ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations" introduces ManiSkill, a benchmark specifically designed to evaluate and improve robotic manipulation skills through generalized learning from 3D visual inputs. This benchmark addresses existing shortcomings in manipulation skill evaluations by incorporating diverse 3D objects and presenting tasks that challenge a variety of manipulation skills. It places an emphasis on intra-class variation of object topology and geometry, a key factor in assessing the object-level generalizability of manipulation policies. The benchmark operates in a physically accurate simulation environment, enabling realistic model evaluations.

Key Features of ManiSkill

ManiSkill offers four primary features that differentiate it from other benchmarks:

1. Diverse 3D Assets: The benchmark includes a selection of 162 objects, sourced from the PartNet-Mobility dataset, separated into training and test sets, ensuring a wide range of object complexity and variation. This diversity tests generalizable manipulation skills effectively across unseen test objects.
2. Variety in Manipulation Tasks: Four manipulation tasks—OpenCabinetDoor, OpenCabinetDrawer, PushChair, and MoveBucket—are provided, each featuring distinct manipulation challenges with diverse motion types. These tasks are constructed to evaluate deviations in robotic interaction approaches necessitated by different mechanical constraints such as revolute and prismatic joints.
3. 3D Visual Input and Ego-Centric Views: Researchers working on 3D deep learning are directly supported by the provision of ego-centric point clouds or RGB-D images captured from robot-mounted panoramic cameras, thereby integrating realistic sensor data into manipulation learning.
4. Extensive Demonstration Dataset: A comprehensive dataset comprising approximately 36,000 successful trajectories and 1.5 million frames is provided to support learning-from-demonstrations. These demonstrations were generated using reinforcement learning techniques tailored to optimize physical interactions.

Numerical Findings and Implications

The experimental results from the ManiSkill benchmark reveal substantial challenges in achieving object-level generalization for manipulation tasks. Simple imitation learning algorithms, such as behavior cloning (BC), demonstrated varied success rates across different tasks, with object-specific learning significantly outperforming attempts at broader generalization. This indicates that current methodologies, even with access to full physical simulations and large trajectory datasets, face limitations in generalizing across diverse object forms and manipulation tasks.

Such findings highlight key areas for development, including enhanced 3D perception models and the optimization of policy learning techniques. The implications for AI and robotics research are considerable, as improvements in these areas could lead to advancements in autonomous robotic systems capable of performing complex tasks in variable real-world environments. Future work deriving from ManiSkill could explore multi-modal approaches combining visual data, touch sensor feedback, and adaptive learning strategies to enhance the capability of robots to generalize across unseen instances within defined categories.

Speculation on Future Developments

While the benchmark itself provides a robust framework and dataset for manipulation skill evaluation, future research could focus more deeply on refining policy learning algorithms to improve generalization. Integrating aspects such as unsupervised learning or meta-learning could significantly bolster adaptability and efficiency in manipulation tasks. Additionally, as simulation fidelity improves, real-world deployment of trained models using sim-to-real transfer techniques could bring the outcomes of manipulation skill learning into practical applications in industries and households alike.

In conclusion, ManiSkill sets the stage for comprehensive research into robotic manipulation skills, offering both a benchmark that measures skill generalizability and a platform upon which future investigations into more complex learning architectures can be built. The paper offers a detailed proposal for how object-level generalization might be systematically explored within the context of physical interaction tasks, paving the way for advancements in generalizable and adaptive robotic systems.