Acquiring prior knowledge of trajectory distributions in specific environments can significantly expedite the optimisation process in robot motion planning. Leveraging successful past plans and utilising trajectory generative models as priors offers a clear advantage. Previous studies have proposed various methods to harness these priors, such as using prior samples for initialisation or incorporating the prior distribution into trajectory optimisation through inference. Recently, diffusion models have demonstrated effectiveness in encoding multi-modal data in high-dimensional settings. In this study, we introduce a methodology that integrates Stein Variational Gradient Descent (SVGD) with Gaussian Process Motion Planning (GPMP), leveraging diffusion models as multi-modal priors. This approach combines the advantages of deep generative model and Bayesian inference to reduce the computation time required to approximate the posterior distribution of trajectories, particularly when adapting to new, unseen environments. In addition, we incorporate path signatures into our method to enhance the diversity of the posterior distribution, thereby improving the optimality of trajectories in multi-modal settings. To validate our approach, we conduct comparative assessments against multiple baseline methods across various scenarios, including 2D planar robots and robotic manipulators.

We propose a novel framework and algorithm for solving classical planning problems with an implicit hierarchical solver based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills that are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suited skill. This principle dynamically creates a hierarchy of plans, in which each skill plans for sub-goals for which it is specialised. Our method performs robustly in noisy environments with non-deterministic action effects and features on-demand execution—skill policies are only evaluated when needed. Plans are only generated at the highest level, then expanded and optimised when the latest state information is available. The high-level plan retains the initial planning intent and previously computed skills, effectively reducing the computation needed to adapt to environmental changes. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains, both in terms of solution length and planning time.

We integrate neural networks into kinodynamic motion planning and present the Learning for KinoDynamic Tree Expansion (L4KDE) method. Tree-based planning approaches, such as rapidly exploring random tree (RRT), are the dominant approach to finding globally optimal plans in continuous state-space motion planning. Central to these approaches is tree expansion, the procedure in which new nodes are added to an ever-expanding tree. We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints. In the interest of quickly selecting nodes to connect newly sampled coordinates, existing methods typically cannot optimise the finding of nodes that have a low cost to transition to sampled coordinates. Instead, they use metrics like Euclidean distance between coordinates as a heuristic for selecting candidate nodes to connect to the search tree. We propose L4KDE to address this issue. L4KDE uses a neural network to predict transition costs between queried states, which can be efficiently computed in batch, providing much higher quality estimates of transition cost compared to commonly used heuristics while maintaining almost-surely asymptotic optimality guarantee. We empirically demonstrate the significant performance improvement provided by L4KDE on a variety of challenging system dynamics,with the ability to generalise across different instances of the same model class and in conjunction with a suite of modern tree-based motion planners.

Probabilistic Movement Primitives (ProMPs) and their variants are powerful methods for enabling robots to learn complex tasks from human demonstrations, where motion trajectories are represented as stochastic processes with Gaussian assumptions. However, despite their computational efficiency, these methods have limited expressiveness in capturing the diversity found in human demonstrations, which are typically characterized by the multi-modality of motions. For example, when picking up an object partially obscured by an obstacle, some individuals may opt to go to the right, while others may choose the left side of the object. In this paper, we introduce Stein Movement Primitives (SMPs), a novel approach to probabilistic movement primitives. We formulate motion primitive adaptation as a non-parametric probabilistic inference using Stein Variational Gradient Descent (SVGD), thus avoiding any explicit posterior distribution assumptions and enabling the direct representation of the multi-modality in human demonstrations. We illustrate how our method can adapt robot motion to different scenarios while maintaining high similarity to the original demonstrations, even when the demonstrations are multi-modal. Experimentally, we demonstrate our approach to several domain adaptation problems using the LASA dataset and with a real robotic arm.

Abstract The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model’s accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field.

Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs), where a flexible function approximator (often a neural network) is used to estimate the system dynamics, given as a time derivative. However, these integrators can be unsatisfactorily slow and unstable when learning systems of ODEs from long sequences. We propose to learn an ODE of interest from data by viewing its dynamics as a vector field related to another base vector field via a diffeomorphism (i.e., a differentiable bijection), represented by an invertible neural network (INN). By learning both the INN and the dynamics of the base ODE, we provide an avenue to offload some of the complexity in modelling the dynamics directly on to the INN. Consequently, by restricting the base ODE to be amenable to integration, we can speed up and improve the robustness of integrating trajectories from the learned system. We demonstrate the efficacy of our method in training and evaluating benchmark ODE systems, as well as within continuous-depth neural networks models. We show that our approach attains speed-ups of up to two orders of magnitude when integrating learned ODEs.

In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying configuration space, affecting all subsequent tasks. Previously free configurations might now be occupied by the manoeuvred objects, and previously occupied space might now open up new paths. We propose Lazy Tree-based Replanner (LTR *)-a novel hybrid planner that inherits the rapid planning nature of existing anytime incremental sampling-based planners. At the same time, it allows subsequent tasks to leverage prior experience via a lazy experience graph. Previous experience is summarised in a lazy graph structure, and LTR * is formulated to be robust and beneficial regard-less of the extent of changes in the workspace. Our hybrid approach attains a faster speed in obtaining an initial solution than existing roadmap-based planners and often with a lower cost in trajectory length. Subsequent tasks can utilise the lazy experience graph to speed up finding a solution and take advant-age of the optimised graph to minimise the cost objective. We provide proofs of probabilistic completeness and almost-surely asymptotic optimal guarantees. Experimentally, we show that in repeated pick-and-place tasks, L T R * attains a high gain in performance when planning for subsequent tasks.

We propose a novel approach for planning agents to compose abstract skills via observing and learning from historical interactions with the world. Our framework operates in a Markov state-space model via a set of actions under unknown pre-conditions. We formulate skills as high-level abstract policies that propose action plans based on the current state. Each policy learns new plans by observing the states' transitions while the agent interacts with the world. Such an approach automatically learns new plans to achieve specific intended effects, but the success of such plans is often dependent on the states in which they are applicable. Therefore, we formulate the evaluation of such plans as infinitely many multi-armed bandit problems, where we balance the allocation of resources on evaluating the success probability of existing arms and exploring new options. The result is a planner capable of automatically learning robust high-level skills under a noisy environment; such skills implicitly learn the action pre-condition without explicit knowledge. We show that this planning approach is experimentally very competitive in high-dimensional state space domains.

Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other’s actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset.

Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The majority of traditional planners are inefficient as they use uninformative sampling distributions instead of exploiting structures and patterns in the problem to guide better sampling strategies. Moreover, most current learning-based planners are susceptible to posterior collapse or mode collapse due to the sparsity and highly varying nature of C-Space and motion plan configurations. This work introduces a conditional normalising flow-based distribution learned through previous experiences, which improves existing methods’ sampling scheme. Our distribution can be conditioned on the current problem instance to provide informative prior to sample configurations within promising regions. When we train our sampler with an expert planner, the resulting distribution is often near-optimal, and the planner can find a solution faster, with less invalid samples and less initial cost. The normalising flow-based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based sampler. Finally, we provide a formulation and theoretical foundation to sample from the distribution efficiently. Experimentally we demonstrate utilising the flow-based distribution in a sampling-based motion planner allows a solution to be found faster, with fewer samples and better overall runtime performance.

Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings. Many motion prediction methods in the literature implicitly account for environmental factors by learning on observed motion in a fixed environment, and are designed to make predictions in the same environment. In this paper, we address the problem of generating likely motion trajectories for novel environments, represented as occupancy grid maps, where motion has not been observed. We introduce the Occupancy-Conditional Trajectory Network (OTNet) framework, capable of transferring the previously observed motion patterns in known environments to new environments. OTNet provides a functional representation for motion trajectories and utilises neural networks to learn occupancy-conditional distributions over the function parameters. We empirically demonstrate our method’s ability to generate complex multi-modal trajectory patterns in both simulated and real-world environments.

Sampling efficiency in a highly constrained environment has long been a major challenge for sampling-based planners. In this work, we propose Rapidly-exploring Random disjointed-Trees*(RRdT*), an incremental optimal multi-query planner. RRdT*uses multiple disjointed-trees to exploit local-connectivity of spaces via Markov Chain random sampling, which utilises neighbourhood information derived from previous successful and failed samples. To balance local exploitation, RRdT*actively explore unseen global spaces when local-connectivity exploitation is unsuccessful. The active trade-off between local exploitation and global exploration is formulated as a multi-armed bandit problem. We argue that the active balancing of global exploration and local exploitation is the key to improving sample efficient in sampling-based motion planners. We provide rigorous proofs of completeness and optimal convergence for this novel approach. Furthermore, we demonstrate experimentally the effectiveness of RRdT*'s locally exploring trees in granting improved visibility for planning. Consequently, RRdT*outperforms existing state-of-the-art incremental planners, especially in highly constrained environments.