Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recalling an earlier visual cue, tracking task progress, counting repeated events, or estimating elapsed time. We present ReCAT, a language-conditioned policy with structured recurrent memory. An instruction-conditioned encoder forms features from the current observation. A recurrent memory integrates the observation stream through Mamba-2 layers and one causal attention layer. A flow-matching Transformer decoder reads the current and the historical representation through separate cross-attention in every block. ReCAT reaches 95.3% average success on LIBERO and 62.4% on RMBench, with the best or tied-best result on six of nine tasks. On three real-robot tasks probing spatial recall, event counting, and interval timing, the best ReCAT variant reaches 66.7% average success, against 8.3% for the strongest short-history baseline. Controlled comparisons within ReCAT show that the observation encoder and every-block memory conditioning are needed for this performance. They also show that update rules developed for efficient sequence modeling behave differently as robot memory: additive updates have the highest observed success on counting and timing, and delta-rule updates on spatial recall.

Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high-frequency modality changes at hundreds of hertz, vision evolves more slowly, and language stays constant across an episode. A synchronous VLA oversamples slow modalities, undersamples fast ones, and caps action generation at the lowest effective frequency. We hypothesize that decoupling temporal processing per modality, letting each update and retain information at its own sensor rate, yields stronger representations and more robust control. We present Decoupled Asynchronous Multimodal Vision Language Action (DAM-VLA), which maintains per-modality latent buffers refreshed at sensor rates and read continuously by the action head, integrating new high-frequency modalities through gated cross-attention that leaves the pretrained backbone intact. Across seven contact-rich real-world manipulation tasks, DAMVLA more than doubles the average success rate of the strongest synchronous baseline (95.2% vs. 40.95%) while sustaining smooth, reactive 100 Hz control

Robotic dexterous grasping with human-level performance remains an unsolved challenge. Imitation Learning (IL) offers a compelling paradigm, yet vision-only approaches are brittle under lighting changes and novel objects. Touch sensing addresses this gap by grounding manipulation in contact properties rather than visual appearance. We present a systematic study of touch sensing in dexterous, multi-fingered IL, evaluating three algorithms (ACT, Flow Matching, BESO) across transformer, Mamba, and xLSTM backbones, and three tactile representations (Vision-only, end-to-end CNN, pretrained Sparsh-X). Using VR teleoperation with a Digit 360-equipped robotic hand, we collect demonstrations and evaluate generalization to novel objects and robustness to lighting perturbations. We train 21 policy variants (7 algorithms-backbone combinations × 3 tactile representations) and evaluate each on the training and test objects, yielding 28 comparison pairs. In 25 of 28, tactile signals match or improve upon vision-only performance, with the largest gains on unseen objects and degraded lighting. Failure analysis reveals that touch primarily improves initial grasp alignment, reducing misplaced approaches. Notably, end-to-end CNN encoders degrade diffusion-based policies by corrupting visual subtasks, while pretrained encoders avoid this. These results provide practical guidance on learning algorithm, backbone, and tactile representation choices for imitation learning of dexterous grasping.

This work introduces Disentangled Diffusion Policy (DisDP), an Imitation Learning (IL) method that enhances robustness by integrating multi-view disentanglement into diffusionbased policies. For robots to be deployed on a large scale across various applications they have to be robust against different perturbations, including sensor noise, complete sensor dropout and environmental variations. Existing IL methods struggle to generalize under such conditions, as they typically assume consistent, noise-free inputs. To address this limitation, DisDP structures sensory inputs into shared and private representations, preserving task-relevant global features while retaining distinct details from individual sensors. Additionally, Disentangled Behavior Cloning (DisBC) is introduced, a disentangled Behavior Cloning (BC) policy, to demonstrate the general applicance of disentanglement for IL. This structured representation improves resilience against sensor dropouts and perturbations. Evaluations on The Colosseum and Libero benchmarks demonstrate that disentangled policies achieve better performance in general and exhibit greater robustness to any perturbations compared to their baseline policies.

This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods

This paper presents improved image processing algorithms for detecting leaf infections and uses k-means clustering for agricultural field classification in a heterogeneous robotic system. The approach leverages a dataset of 3,150 crop disease images across three crop types, aiming to enable early disease detection and support mixed cropping via smart farming technologies.