EARL: Eye-on-Hand Reinforcement Learner for Dynamic Grasping with Active Pose Estimation

In this paper, we explore the dynamic grasping of moving objects through active pose tracking and reinforcement learning for hand-eye coordination systems. Most existing vision-based robotic grasping methods implicitly assume target objects are stationary or moving predictably. Performing grasping of unpredictably moving objects presents a unique set of challenges. For example, a pre-computed robust grasp can become unreachable or unstable as the target object moves, and motion planning must also be adaptive. In this work, we present a new approach, Eye-on-hAnd Reinforcement Learner (EARL), for enabling coupled Eye-on-Hand (EoH) robotic manipulation systems to perform real-time active pose tracking and dynamic grasping of novel objects without explicit motion prediction. EARL readily addresses many thorny issues in automated hand-eye coordination, including fast-tracking of 6D object pose from vision, learning control policy for a robotic arm to track a moving object while keeping the object in the camera's field of view, and performing dynamic grasping. We demonstrate the effectiveness of our approach in extensive experiments validated on multiple commercial robotic arms in both simulations and complex real-world tasks.Comment: Presented on IROS 2023 Corresponding author Siddarth Jai

Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning

We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree search (MCTS) is an effective heuristic search algorithm for solving episodic decision-making problems whose underlying search spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS introduces massive parallelism into MCTS for solving planning tasks through the batched execution of a large number of concurrent simulations, which allows for more efficient and accurate evaluations of the expected cost-to-go over large action spaces. When applied to the challenging manipulation tasks of object retrieval from clutter, PMBS achieves a speedup of over $30\times$ with an improved solution quality, in comparison to a serial MCTS implementation. We show that PMBS can be directly applied to real robot hardware with negligible sim-to-real differences. Supplementary material, including video, can be found at https://github.com/arc-l/pmbs.Comment: Accepted for IROS 202

Analysis on the influencing factors of forest natural regeneration in northern China

The purpose of this paper is to clarify that the differences in natural regeneration between the two forests in northern China are caused by precipitation, altitude, temperature, and canopy density. Similarly, the regeneration ability is related to the composition of tree species in the forest. Generally, the growth trend of a hardwood forest is not as good as that of a conifer forest in the northern cold area. At the same time, we also explored the possibility of human disturbance in forests. Due to the different geographical locations of the two forests, the human disturbance of Tuoketuo forest farm is more serious, which is also one of the reasons for the weak forest regeneration ability. By comparing the natural regeneration data of Guandi mountain and the natural regeneration data of Tuoketuo Forest Farm collected by previous scholars, the results showed that: (1) there were 17 seedlings and 44 saplings in ten Guandishan 20 m × 20 m square sampling plots. 9 plot has the highest regeneration density was 250 stems/ ha which canopy density was 0.25. When canopy density higher than 0.25, the regeneration density decreased significantly with the increase of canopy density; when canopy density higher than 0.80, there was no regeneration. (2) Four 1 × 1 (m) (1 m2) small plots were set in 2 × 2 (m2) plant plots to examine the natural regeneration frequency of Tuoketuo county. The total area of regeneration is 10756.8 ha, the total number of regeneration plants is 236650 stems, and the average number of regeneration stems per hectare was only 22 stems. (3) The regeneration quantity of young forest and seedling in Guandishan mountain forest is more. Light, altitude, and other factors were the main factors affecting the regeneration of Larix principis-rupprechtii Mayr on Guandishan Mountain. The forest in Tuoketuo county is not only affected by stand structure, site conditions, climate and other natural conditions but also affected by human disturbance

Lightweight Machine Learning for Seizure Detection on Wearable Devices

For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately detect epileptic seizures. However, training deep learning networks requires a large amount of data and computing resources, which is a major challenge for resource-constrained wearable systems. In this paper, we propose EpilepsyNet, the first interpretable self-supervised network tailored to resource-constrained devices without using any seizure data in its initial offline training. At runtime, however, once a seizure is detected, it can be incorporated into our self-supervised technique to improve seizure detection performance, without the need to retrain our learning model, hence incurring no energy overheads. Our self-supervised approach can reach a detection performance of 79.2%, which is on par with the state-of-the-art fully-supervised deep neural networks trained on seizure data. At the same time, our proposed approach can be deployed in resource-constrained wearable devices, reaching up to 1.3 days of battery life on a single charge