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Teleoperating a robot for complex and intricate tasks demands a high mental workload from a human operator. Deploying multiple operators can mitigate this problem, but it can be also a costly solution. Learning from Demonstrations can reduce the human operator's burden by learning repetitive teleoperation tasks. Yet, the demonstrations via teleoperation tend to be inconsistent compared to other modalities of human demonstrations. In order to handle less consistent and asynchronous demonstrations effectively, this paper proposes a learning scheme based on Dynamic Movement Primitives. In particular, a new Expectation Maximization algorithm which synchronizes and encodes demonstrations with high temporal and spatial variances is proposed. Furthermore, we discuss two shared teleoperation architectures, where, instead of multiple human operators, a learned artificial agent and a human operator share authority over a task while teleoperating cooperatively. The agent controls the more mundane and repetitive motion in the task whereas human takes charge of the more critical and uncertain motion. The proposed algorithm together with the two shared teleoperation architectures (human-synchronized and agent-synchronized shared teleoperation) has been tested and validated through simulation and experiments on 3 Degrees-of-Freedom Phantom-to-Phantom teleoperation. Conclusively, the both proposed shared teleoperation architectures have shown superior performance when compared with the human-only teleoperation for a peg-in-hole task.

We propose an online incremental learning approach through teleoperation which allows an operator to partially modify a learned model, whenever it is necessary, during task execution. Compared to conventional incremental learning approaches, the proposed approach is applicable for teleoperation-based teaching and it needs only partial demonstration without any need to obstruct the task execution. Dynamic authority distribution and kinesthetic coupling between the operator and the agent helps the operator to correctly perceive the exact instance where modification needs to be asserted in the agent's behaviour online using partial trajectory. For this, we propose a variation of the Expectation-Maximization algorithm for updating original model through mini batches of the modified partial trajectory. The proposed approach reduces human workload and latency for a rhythmic peg-in-hole teleoperation task where online partial modification is required during the task operation.

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