Contrastive Language, Action, and State Pre-training for Robot Learning

In this paper, we introduce a method for unifying language, action, and state information in a shared embedding space to facilitate a range of downstream tasks in robot learning. Our method, Contrastive Language, Action, and State Pre-training (CLASP), extends the CLIP formulation by incorporating distributional learning, capturing the inherent complexities and one-to-many relationships in behaviour-text alignment. By employing distributional outputs for both text and behaviour encoders, our model effectively associates diverse textual commands with a single behaviour and vice-versa. We demonstrate the utility of our method for the following downstream tasks: zero-shot text-behaviour retrieval, captioning unseen robot behaviours, and learning a behaviour prior for language-conditioned reinforcement learning. Our distributional encoders exhibit superior retrieval and captioning performance on unseen datasets, and the ability to generate meaningful exploratory behaviours from textual commands, capturing the intricate relationships between language, action, and state. This work represents an initial step towards developing a unified pre-trained model for robotics, with the potential to generalise to a broad range of downstream tasks

Tree diversity and regeneration status in the different forest types of Kotla watershed (Uttarkashi, Uttarakhand, India)

The present study aimed to assess the diversity and regeneration status of tree species in different forest types viz., Pinus forest (PF), Pinus-Oak mixed forest (POF) and Deodar forest (DF) of Kotla watershed (Barkot, Uttarkashi, Uttarakhand). The data were collected through quadrat method and analyzed quantitatively. A total of 28 tree species, belonging to 21 families were recorded in the sampling area (3 forests  10 plots in each  plot size 400 m2). Fagaceae, Pinaceae, Ericaceae, Fabaceae and Juglandaceae were the major families (in terms of number of species). The species-area curves (SACs) of PF and DF reached an asymptote while it predicted more species (din not reach asymptote) in case of POF. The resulted values of different diversity indices (i.e., Dominance, Simpson, Shannon, Evenness, Margalef, and Equitability) revealed that the POF was most diverse (in tree diversity) followed by PF and DF. The ranked species abundance (RSA) curve of POF was log normal type but geometric series type for PF and DF. The density-diameter curves (d-d curves) was reverse J-shaped for POF while in case of PF and DF, the higher densities were observed for middle DBH classes in comparison to lowest and highest DBH classes. The overall regeneration status of the forests in the area was fair (25.8% tree, 18.6% saplings and 55.6% seedlings). The present study provides a deeper understanding of tree diversity pattern and regeneration status from a pocket of Indian Himalayan Region (IHR)

Multiple myeloma: the disease and its treatment

Multiple myeloma represents a malignant proliferation of plasma cells derived from a single clone. The tumor, its products and the host response to it result in a number of organ dysfunctions and symptoms of bone pain, fracture, anemia, hypercalcemia, susceptibility to infection, neurologic symptoms, clotting abnormalities and manifestations of hyperviscosity. The cause of myeloma remains unexplained but it is associated with few occupations, inflammatory conditions, autoimmune illnesses, viral infections and genetic heterogeneity. Direct interaction between multiple myeloma cells and bone marrow cells activates pleiotropic signalling pathways that mediate growth, survival, migration of multiple myeloma cells and also resistance to chemotherapy. Although myeloma remains incurable, but the use of novel drugs like thalidomide, lenalidomide and bortezomib have resulted in a paradigm change in the therapy of myeloma. Their inclusion in current multiple myeloma treatment regimens have extended median overall survival especially in younger patient population. Recent advances in the molecular genetics have provided opportunities to design highly specific inhibitors of signal transduction pathways that may enhance the efficacy of standard chemotherapy drugs by reducing or altering the pathways associated with cell survival. Despite therapeutic advances, multiple myeloma ultimately relapses and remains an incurable disease. Current research goals are to further increase our knowledge, to identify additional targeted therapies, and to reduce adverse effects and improve response rate. This review focuses on recent clinical advancement in ant myeloma strategies with additional discussion dedicated to emerging drugs that may prove beneficial to patients with this disease

Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/homeComment: Accepted for presentation at IROS2020. Project site available at https://sites.google.com/view/mcf-nav/hom

Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We present an extension of the residual reinforcement learning framework from the robotic manipulation literature and adapt it to the vast and unstructured environments that mobile robots can operate in. The concept is based on learning a residual control effect to add to a typical sub-optimal classical controller in order to close the performance gap, whilst guiding the exploration process during training for improved data efficiency. We exploit this tight coupling and propose a novel deployment strategy, switching Residual Reactive Navigation (sRRN), which yields efficient trajectories whilst probabilistically switching to a classical controller in cases of high policy uncertainty. Our approach achieves improved performance over end-to-end alternatives and can be incorporated as part of a complete navigation stack for cluttered indoor navigation tasks in the real world. The code and training environment for this project is made publicly available at https://sites.google.com/view/srrn/home.Comment: Accepted as a conference paper at ICRA2020. Project site available at https://sites.google.com/view/srrn/hom

Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert demonstrations and are embedded into a latent space from which they can be sampled as actions by a high-level RL agent. However, this skill space is expansive, and not all skills are relevant for a given robot state, making exploration difficult. Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space. We firstly propose accelerating exploration in the skill space using state-conditioned generative models to directly bias the high-level agent towards only sampling skills relevant to a given state based on prior experience. Next, we propose a low-level residual policy for fine-grained skill adaptation enabling downstream RL agents to adapt to unseen task variations. Finally, we validate our approach across four challenging manipulation tasks that differ from those used to build the skill space, demonstrating our ability to learn across task variations while significantly accelerating exploration, outperforming prior works. Code and videos are available on our project website: https://krishanrana.github.io/reskill.Comment: 6th Conference on Robot Learning (CoRL), 202

Characterization of fragment emission in ^{20}Ne (7 - 10 MeV/nucleon) + ^{12}C reactions

The inclusive energy distributions of the complex fragments (3 $\leq$ Z $\leq$ 7) emitted from the bombardment of ^{12}C by ^{20}Ne beams with incident energies between 145 and 200 MeV have been measured in the angular range 10$^{o} \leq \theta_{lab} \leq$ 50^{o}. Damped fragment yields in all the cases have been found to be the characteristic of emission from fully energy equilibrated composites. The binary fragment yields are compared with the standard statistical model predictions. Enhanced yields of entrance channel fragments (5 $\leq$ Z $\leq$ 7) indicate the survival of orbiting-like process in ^{20}Ne + ^{12}C system at these energies.Comment: 18 pages, 13 figure

Survival of orbiting in 20^{20}Ne (7 - 10 MeV/nucleon) + 12^{12}C reactions

The inclusive energy distributions of fragments with Z $\geq$ 3 emitted from the bombardment of $^{12}$C by $^{20}$Ne beams with incident energies between 145 and 200 MeV have been measured in the angular range $\theta_{lab} \sim$ 10$^\circ$ - 50$^\circ$. Damped fragment yields in all cases have been found to be characteristic of emission from fully energy equilibrated composites; for B, C fragments, average Q-values, $$, were independent of the centre of mass emission angle ($\theta_{c.m}$), and the angular distributions followed $\sim$1/sin$\theta_{c.m}$ like variation, signifying long life times of the emitting di-nuclear systems. Total yields of these fragments have been found to be much larger compared to the standard statistical model predictions of the same. This may be indicative of the survival of orbiting like process in $^{12}$C + $^{20}$Ne system at these energies.Comment: 7 pages, 5 figures, accepted for publication in Phys. Rev. C (Rapid Communication

Regeneration Patterns of Tree Species Along an Elevational Gradient in the Garhwal Himalaya

This study assessed the regeneration status of tree species at different elevations in Himalayan forests. For this purpose, we assessed the densities of seedlings, saplings, and adult trees of various forest-forming species to understand their population structure and regeneration patterns. Five elevational ranges—3500 m above sea level—were selected in various ranges in the Bhagirathi River catchment area in the Garhwal Himalaya. The highest species richness was recorded at the lowest elevational range, and the lowest species richness was recorded at the highest elevational range. Species diversity, measured using the Simpson and Shannon–Wiener diversity indices, was highest at the lowest elevations and lowest at the highest elevations. Abies spectabilis, Cedrus deodara, Rhododendron arboreum, Pinus roxburghii, and Quercus oblongata were dominant and widely adapted with appropriate regeneration potential at various elevations, whereas Aesculus indica, Juglans regia, and Sorbus cuspidata showed less ability to regenerate, indicating a threat to their survival in the near future. Tree species of subalpine forests Abies pindrow, A. spectabilis, Acer acuminatum, Betula utilis, and R. arboreum were observed to expand their upper limits into alpine meadows. Weak regeneration by some dominant tree species, and expansion by a few less-dominant or even rare species, indicate likely future compositional changes in Himalayan forests

Exclusive light particle measurements for the system 19^{19}F + 12^{12}C at 96 MeV

Decay sequence of hot {31}^P nucleus has been investigated through exclusive light charged particle measurements in coincidence with individual evaporation residues using the reaction {19}^F (96 MeV) + {12}^C. Information on the sequential decay chain have been extracted by confronting the data with the predictions of the statistical model. It is observed from the present analysis that such exclusive light charged particle data may be used as a powerful tool to probe the decay sequence of the hot light compound systems.Comment: 13 pages, 8 figures, Physical Review C (in press