Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation (1710.06422v2)

Abstract: Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.

Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation

The paper "Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation" presents a novel approach to the challenge of instance grasping in cluttered environments using deep learning techniques. The authors propose a framework that leverages multi-task domain adaptation to enable the transfer of learned models from simulated environments to real-world robotic systems. The significance of this work lies in addressing the inherent challenges of domain shifts between simulation and reality, which are prevalent due to disparities in sensory inputs and physical dynamics.

Overview of the Framework

The proposed framework focuses on predicting the probability of successful grasping of specified objects in cluttered scenes, using monocular RGB images and instance segmentation masks as inputs. This approach utilizes three main components: a deep neural network for instance grasp prediction, a domain-adversarial loss function, and training that incorporates both indiscriminate and instance grasping data from simulated and real domains. The neural network architecture benefits from shared parameters, facilitating the transfer of grasping knowledge across different domains.

Technical Contributions

1. Multi-Task Domain Adaptation Framework: The framework integrates multi-task learning with domain adaptation by training the neural network on three task domains: real-world indiscriminate grasping, simulated indiscriminate grasping, and simulated instance grasping. The introduction of domain-adversarial loss aids in minimizing the domain shift, thereby improving the model's generalizability when applied to real-world tasks.
2. Neural Network Design: The architecture processes monocular RGB inputs along with segmentation masks to evaluate the likelihood of a successful grasp. The framework innovatively incorporates single initial segmentation masks to guide the grasping process, rather than requiring continuous updates, which is both computationally efficient and experimentally validated.
3. Automatic Dataset Collection through Simulation: The paper demonstrates the efficacy of using simulated environments to collect valuable labeled data, diminishing the need for extensive real-world data collection, which is often burdened with high costs and logistical challenges.

Experimental Evaluation

The paper's model was evaluated on a robot platform tasked with grasping various household dishware items—both familiar and novel to the model. Impressively, the model achieved a 60.8% grasping success rate, showcasing its robust performance and generalization capabilities to previously unseen objects. This performance underscores the potential of combining domain adaptation strategies with deep learning to tackle real-world robotic manipulation tasks.

Discussion and Implications

The paper opens avenues for further investigation into domain adaptation and transfer learning within robotics. The use of adversarial loss to achieve feature-level transferability between simulation and the real world is particularly promising. This work presents a strong case for employing synthetic data to accelerate real-world applicability, with potential expansion to other manipulation tasks or environments.

The methodology paves the way for enhancing robotic autonomy in complex environments without intensive manual data labeling. Future advancements may explore variations in input modalities, such as depth information, or alternative domains. Additionally, ongoing improvements in computational power and simulation fidelity could further bridge the simulation-to-real gap, making this approach more effective and widespread in practical applications.

In conclusion, the paper presents a substantial contribution to the field of robotic manipulation by integrating domain adaptation techniques with multi-task learning in deep neural networks. Its approach holds promise for reducing reliance on labor-intensive data collection methods and bolstering the versatility of robotic grasping systems across diverse real-world scenarios.