Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning

Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) a state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple REachability discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.Comment: Accepted by 2023 RAL with ICR

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://repaintnerf.github.io for a better view of our results.Comment: IJCAI 2023 Accepted (Main Track

Spectroscopic and Photometric Observations of Unidentified Ultraviolate Variable Objects in GUVV-2 Catalog

An NUV-optical diagram made for sources from the secend Galaxy Evolution Explorer (GALEX) Ultraviolet Variability (GUVV-2) Catalog provide us a method to tentatively classify the unknown GUVV2 sources by their NUV-optical magnitudes. On the purpose of testing the correctness and generality of the method, we carry out a program on the spectroscopic observations of the unidentified GUVV2 sources. The spectroscopic identification for these 37 sources are 19 type -A to -F stars, 10 type -G to -K stars and 7 M dwarf stars together with an AGN. We also present the light curves in R-band for two RR Lyrae star candidates selected from the NUV-optical diagram, both of which perform cyclic variations. Combining there light curves and colors, we classify them as RR Lyrae stars. To confirm the results, we shows a color-color diagram for the 37 newly spectroscopically identified objects compared with the previously identified ones, which manifests good consistence with our former results, indicating that the ultroviolet variable sources can be initially classified by their NUV/optical color-color diagram.Comment: 10 pages, 7 figure

Few-photon imaging at 1550 nm using a low-timing-jitter superconducting nanowire single-photon detector

We demonstrated a laser depth imaging system based on the time-correlated single-photon counting technique, which was incorporated with a low-jitter superconducting nanowire single-photon detector (SNSPD), operated at the wavelength of 1550 nm. A sub-picosecond time-bin width was chosen for photon counting, resulting in a discrete noise of less than one/two counts for each time bin under indoor/outdoor daylight conditions, with a collection time of 50 ms. Because of the low-jitter SNSPD, the target signal histogram was significantly distinguishable, even for a fairly low retro-reflected photon flux. The depth information was determined directly by the highest bin counts, instead of using any data fitting combined with complex algorithms. Millimeter resolution depth imaging of a low-signature object was obtained, and more accurate data than that produced by the traditional Gaussian fitting method was generated. Combined with the intensity of the return photons, three-dimensional reconstruction overlaid with reflectivity data was realized.Comment: 7 pages, 9 figure

Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling

Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these computational challenges, a less-explored issue is designing an efficient and adaptable network backbone for iterative refinement. Current options like U-Net and Vision Transformer often rely on resource-intensive deep networks and lack the flexibility needed for generating images at variable resolutions or with a smaller network than used in training. This study introduces LEGO bricks, which seamlessly integrate Local-feature Enrichment and Global-content Orchestration. These bricks can be stacked to create a test-time reconfigurable diffusion backbone, allowing selective skipping of bricks to reduce sampling costs and generate higher-resolution images than the training data. LEGO bricks enrich local regions with an MLP and transform them using a Transformer block while maintaining a consistent full-resolution image across all bricks. Experimental results demonstrate that LEGO bricks enhance training efficiency, expedite convergence, and facilitate variable-resolution image generation while maintaining strong generative performance. Moreover, LEGO significantly reduces sampling time compared to other methods, establishing it as a valuable enhancement for diffusion models

EFFECTS OF LONG-TERM TAI CHI EXERCISE ON BALANCE CONTROL IN OLDER ADULTS

This study assessed the static and dynamic balance control of older adults who have 10 years of Tai Chi exercise experience and compared their characteristics with their sedentary counterparts. The abilities were measured using methods: single-leg stance times with eyes open and closed; sway of center of pressure (COP) during static standing with eyes open/closed, and leaning the body in three specific directions. Compared with control group, 1) Tai Chi Group showed longer single-leg stance times with eyes open and closed, 2) slower sway velocity of COP in mediolateral and anterioposterior directions and shorter sway distance in both directions, and 3) shorter total, anterioposterior, and mediolateral routes and shorter time spent during the dynamic balance test. Long-term Tai Chi exercise improves the balance ability, especially the dynamic balance, of older adults

Efficient Characterizations of Multiphoton States with Ultra-thin Integrated Photonics

Metasurface enables the generation and manipulation of multiphoton entanglement with flat optics, providing a more efficient platform for large-scale photonic quantum information processing. Here, we show that a single metasurface optical chip would allow more efficient characterizations of multiphoton entangled states, such as shadow tomography, which generally requires fast and complicated control of optical setups to perform projective measurements in different bases, a demanding task using conventional optics. The compact and stable device here allows implementations of general positive observable value measures with a reduced sample complexity and significantly alleviates the experimental complexity to implement shadow tomography. Integrating self-learning and calibration algorithms, we observe notable advantages in the reconstruction of multiphoton entanglement, including using fewer measurements, having higher accuracy, and being robust against optical loss. Our work unveils the feasibility of metasurface as a favorable integrated optical device for efficient characterization of multiphoton entanglement, and sheds light on scalable photonic quantum technologies with ultra-thin integrated optics.Comment: 15 pages, 9 figure