Filter-based shared control aims to accept and augment an operator's ability to control a robot. Current solutions accept actions based on their direction aligning with the robot's optimal policy. These strategies reject a human's small corrective actions if they conflict with the robot's direction and accept too aggressive actions as long as they are consistent with the robot's direction. Such strategies may cause task failures and the operator's feeling of loss of control. To close the gap, we propose WE-Filter, which has flexible, adaptive criteria allowing the operator's small corrective actions and tempering too aggressive ones. Inspired by classical work-energy impact problems between two dynamic, interactive bodies, both inputs' properties (direction and magnitude) are inherently considered, creating intuitive, adaptive bounds to accept sensible actions. The model identifies behaviors before and after impact. The rationale is that each timestep of shared control acts as an impact between the operator's and the robot's policies, where post-impact behaviors depend on their previous behaviors. As time continues, a series of impacts occur. The aim is to minimize impacts that occur to reach an agreement faster and reduce strong reactionary behaviors. Our model determines flexible acceptance criteria to bound a mismatch of magnitude and finds a replacement action for conflicting policies. The WE-Filter achieves better task performance, the ratio of accepted actions, and action similarity than the existing methods.

In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning-based approaches mainly focus on training a single robot-structure-specific policy through the centralized learning mechanism, lacking adaptability to changes like robot malfunction. To solve this limitation, this work treats each finger as an individual agent and trains multiple agents to control their assigned fingers to complete the in-hand manipulation task cooperatively. We propose the Multi-Agent Global-Observation Critic and Local-Observation Actor (MAGCLA) method, where the critic can observe all agents' actions globally, and the actor only locally observes its neighbors' actions. Besides, conventional individual experience replay may cause unstable cooperation due to the asynchronous performance increment of each agent, which is critical for in-hand manipulation tasks. To solve this issue, we propose the Synchronized Hindsight Experience Replay (SHER) method to synchronize and efficiently reuse the replayed experience across all agents. The methods are evaluated in two in-hand manipulation tasks on the Shadow dexterous hand. The results show that SHER helps MAGCLA achieve comparable learning efficiency to a single policy, and the MAGCLA approach is more generalizable in different tasks. The trained policies have higher adaptability in the robot malfunction test compared to the baseline multi-agent and single-agent approaches.

While a human is tracking a moving object to prepare for later grasping, we naturally change our hand pose to generate optimal pre-grasp to avoid post-grasp adjustment. Robot hand controllers need dynamic pre-grasp planning capability, so they are not limited in dynamic tracking and catching tasks. To fill this gap, we explore the feasibility of using a two-stage optimization method to enable dynamic pre-grasp planning of individual fingers while tracking a moving object to ensure a later successful grasp. The first stage adopts multi-agent pursuit to partition the search space on the object surface. The method allows each finger to consider its immediate surroundings in a local view instead of globally determining the best location for all fingers. The search space for each finger is dramatically reduced since sensible alternatives are the ones left after pruning. Each finger goal location acts independently yet coordinates with others to achieve the goal of covering the object. In the second stage, four different goal point movement strategies are presented to impact the finger goal location in their respective search space to demonstrate the ability to facilitate different needs of the task and requirements of the designer. Dynamic finger goal adaption is obtained by iteratively updating these two stages. The approach is consistent in different scenarios for the object.

Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner. In existing research, the robot powered by DRL adopts coupled observation of the environment and the human partner to learn both dynamics simultaneously. However, such a learning strategy is limited in terms of learning efficiency and team performance. This work proposes a novel task decomposition method with a hierarchical reward mechanism that enables the robot to learn the hierarchical dynamic control task separately from learning the human partner’s behavior. The method is validated with a hierarchical control task in a simulated environment with human subject experiments. Our method also provides insight into the design of the learning strategy for HRC. The results show that the robot should learn the task first to achieve higher team performance and learn the human first to achieve higher learning efficiency.

Promoting a robot agent's autonomy level, which allows it to understand the human operator's intent and provide motion assistance to achieve it, has demonstrated great advantages to the operator's intent in teleoperation. However, the research has been limited to the target approaching process. We advance the shared control technique one step further to deal with the more challenging object manipulation task. Appropriately manipulating an object is challenging as it requires fine motion constraints for a certain manipulation task. Although these motion constraints are critical for task success, they are subtle to observe from ambiguous human motion. The disembodiment problem and physical discrepancy between the human and robot hands bring additional uncertainty, make the object manipulation task more challenging. Moreover, there is a lack of modeling and planning techniques that can effectively combine the human motion input and robot agent's motion input while accounting for the ambiguity of the human intent. To overcome this challenge, we built a multi-task robot grasping model and developed an intent-uncertainty-aware grasp planner to generate robust grasp poses given the ambiguous human intent inference inputs. With this validated modeling and planning techniques, it is expected to extend teleoperated robots' functionality and adoption in practical telemanipulation scenarios.

In the context of object-oriented analysis and design (OOAD), class responsibility assignment is not an easy skill to acquire. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. Our objective is to provide decision-making support to reassign methods and attributes to classes in a class diagram. Our solution is based on a multi-objective genetic algorithm (MOGA) and uses class coupling and cohesion measurement for defining fitness functions. Our MOGA takes as input a class diagram to be optimized and suggests possible improvements to it. The choice of a MOGA stems from the fact that there are typically many evaluation criteria that cannot be easily combined into one objective, and several alternative solutions are acceptable for a given OO domain model. Using a carefully selected case study, this paper investigates the application of our proposed MOGA to the class responsibility assignment problem, in the context of object-oriented analysis and domain class models. Our results suggest that the MOGA can help correct suboptimal class responsibility assignment decisions and perform far better than simpler alternative heuristics such as hill climbing and a single-objective GA.

Class responsibility assignment is not an easy skill to acquire. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. Our objective is to provide decision-making help to re-assign methods and attributes to classes in a class diagram. Our solution is based on a multi-objective genetic algorithm (MOGA) and uses class coupling and cohesion measurement. Our MOGA takes as input a class diagram to be optimized and suggests possible improvements to it. The choice of a MOGA stems from the fact that there are typically many evaluation criteria that cannot be easily combined into one objective, and several alternative solutions are acceptable for a given OO domain model. This article presents our approach in detail, our decisions regarding the multi-objective genetic algorithm, and reports on a case study. Our results suggest that the MOGA can help correct suboptimal class responsibility assignment decisions.

This paper describes Disciple, an Artificial Intelligence based decision aid which subject-matter experts can train and use when making decisions under stressful, complex, and constrained conditions. The tool was developed and used under the Defense Advanced Research Projects Agency's High Performance Knowledge Base and Rapid Knowledge Formation programs. Some domains in which the tool would be applicable are described, with particular emphasis on military battle planning. The paper concludes with a discussion of future trends in decision-support application tools.