Improvements to the Overpotential of All-Solid-State Lithium-Ion Batteries during the Past Ten Years

After the research that shows that Li10GeP2S12 (LGPS)-type sulfide solid electrolytes can reach the high ionic conductivity at the room temperature, sulfide solid electrolytes have been intensively developed with regard to ionic conductivity and mechanical properties. As a result, an increasing volume of research has been conducted to employ all-solid-state lithium batteries in electric automobiles within the next five years. To achieve this goal, it is important to review the research over the past decade, and understand the requirements for future research necessary to realize the practical applications of all-solid-state lithium batteries. To date, research on all-solid-state lithium batteries has focused on achieving overpotential properties similar to those of conventional liquid-lithium-ion batteries by increasing the ionic conductivity of the solid electrolytes. However, the increase in the ionic conductivity should be accompanied by improvements of the electronic conductivity within the electrode to enable practical applications. This essay provides a critical overview of the recent progress and future research directions of the all-solid-state lithium batteries for practical applications

Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion

The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the foot of humanoids can be measured using a force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and failure possibility of the system. This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. For learning, the GRU network is used and random walking data is collected. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors compared to the model-based method, momentum observer (MOB) with friction modeling. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control, enabling stable walking. Moreover, even when the robot's feet and the inertia of the upper body are changed, the trained network shows consistent performance with a model-based calibration. This result demonstrates the possibility of removing FTS on the robot, which reduces the disadvantages of hardware sensors. The summary video is available at https://youtu.be/gT1D4tOiKpo.Comment: Accepted by 2023 IROS conferenc

Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators

This paper describes the control, and evaluation of a new human-scaled biped robot with liquid cooled viscoelastic actuators (VLCA). Based on the lessons learned from previous work from our team on VLCA [1], we present a new system design embodying a Reaction Force Sensing Series Elastic Actuator (RFSEA) and a Force Sensing Series Elastic Actuator (FSEA). These designs are aimed at reducing the size and weight of the robot's actuation system while inheriting the advantages of our designs such as energy efficiency, torque density, impact resistance and position/force controllability. The system design takes into consideration human-inspired kinematics and range-of-motion (ROM), while relying on foot placement to balance. In terms of actuator control, we perform a stability analysis on a Disturbance Observer (DOB) designed for force control. We then evaluate various position control algorithms both in the time and frequency domains for our VLCA actuators. Having the low level baseline established, we first perform a controller evaluation on the legs using Operational Space Control (OSC) [2]. Finally, we move on to evaluating the full bipedal robot by accomplishing unsupported dynamic walking by means of the algorithms to appear in [3].Comment: 8 pages, 8 figure

Emission of Spin-correlated Matter-wave Jets from Spinor Bose-Einstein Condensates

We report the observation of matter-wave jet emission in a strongly ferromagnetic spinor Bose-Einstein condensate of $^7$Li atoms. Directional atomic beams with $|{F=1,m_F=1}\rangle$ and $|{F=1,m_F=-1}\rangle$ spin states are generated from $|{F=1,m_F=0}\rangle$ state condensates, or vice versa. This results from collective spin-mixing scattering events, where spontaneously produced pairs of atoms with opposite momentum facilitates additional spin-mixing collisions as they pass through the condensates. The matter-wave jets of different spin states ($|{F=1,m_F=\pm1}\rangle$) can be a macroscopic Einstein-Podolsky-Rosen state with spacelike separation. Its spin-momentum correlations are studied by using the angular correlation function for each spin state. Rotating the spin axis, the inter-spin and intra-spin momentum correlation peaks display a high contrast oscillation, indicating collective coherence of the atomic ensembles. We provide numerical calculations that describe the experimental results at a quantitative level and can identify its entanglement after 100~ms of a long time-of-flight.Comment: 13 pages(6 main text, 7 supplemental material), 12 figure

Direct Preference-based Policy Optimization without Reward Modeling

Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is challenging. Existing PbRL methods generally involve a two-step procedure: they first learn a reward model based on given preference data and then employ off-the-shelf reinforcement learning algorithms using the learned reward model. However, obtaining an accurate reward model solely from preference information, especially when the preference is from human teachers, can be difficult. Instead, we propose a PbRL algorithm that directly learns from preference without requiring any reward modeling. To achieve this, we adopt a contrastive learning framework to design a novel policy scoring metric that assigns a high score to policies that align with the given preferences. We apply our algorithm to offline RL tasks with actual human preference labels and show that our algorithm outperforms or is on par with the existing PbRL methods. Notably, on high-dimensional control tasks, our algorithm surpasses offline RL methods that learn with ground-truth reward information. Finally, we show that our algorithm can be successfully applied to fine-tune large language models.Comment: NeurIPS 202

Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms

Commonsense norms are defeasible by context: reading books is usually great, but not when driving a car. While contexts can be explicitly described in language, in embodied scenarios, contexts are often provided visually. This type of visually grounded reasoning about defeasible commonsense norms is generally easy for humans, but (as we show) poses a challenge for machines, as it necessitates both visual understanding and reasoning about commonsense norms. We construct a new multimodal benchmark for studying visual-grounded commonsense norms: NORMLENS. NORMLENS consists of 10K human judgments accompanied by free-form explanations covering 2K multimodal situations, and serves as a probe to address two questions: (1) to what extent can models align with average human judgment? and (2) how well can models explain their predicted judgments? We find that state-of-the-art model judgments and explanations are not well-aligned with human annotation. Additionally, we present a new approach to better align models with humans by distilling social commonsense knowledge from large language models. The data and code are released at https://seungjuhan.me/normlens.Comment: Published as a conference paper at EMNLP 2023 (long

Surface and Interfacial Chemistry in the Nickel-Rich Cathode Materials

With increasing demands for high energy lithium-ion batteries, layered nickel-rich cathode materials have been considered as the most promising candidate due to their high reversible capacity and low cost. Although some of the materials with nickel contents <= 60 % were commercialized, there are tremendous obstacles for further improvement of electrochemical performance, which is strongly related to the unstable cathode surface and interfacial properties. In this regard, a specific review on the interfacial chemistry between the cathode and electrolyte during electrochemical testing is provided. We highlight the underpinning interfacial chemistry and degradation mechanisms of the cathode materials. Finally, light is shed on the recent efforts for enhancing the interfacial stability of the nickel-rich cathode materials