Publications

Below is my list of publications organized by topics. They come with pre-prints (even post-prints with some corrections made after publication) and source code. I'm glad most robotics works nowadays are available on arXiv or HAL. I have some thoughts on how we can take it to the next level.

Recent works

Walking stabilization

Contact stability

Walking trajectory generation

Multi-contact motion control

Motion planning

Machine learning

Further topics

Force sensing
  • Multi-Contact Interaction Force Sensing from Whole-Body Motion Capture
    Tu-Hoa Pham, Stéphane Caron and Abderrahmane Kheddar. IEEE Transactions on Industrial Informatics. Submitted November 2016. Published October 2017. (pdf)

    We present a novel technique that unobtrusively estimates forces exerted by human participants in multi-contact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to setup cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly challenging, as the knowledge of a given motion only characterizes the resultant force, which can generally be caused by an infinity of force distributions over individual contacts. We collect and release a large-scale dataset on how humans instinctively regulate interaction forces on diverse multi-contact tasks and motions. The force estimation framework we propose leverages physics-based optimization and neural networks to reconstruct force distributions that are physically realistic and compatible with real interaction force patterns. We show the effectiveness of our approach on various locomotion and multi-contact scenarios.

  • Whole-Body Contact Force Sensing From Motion Capture
    Tu-Hoa Pham, Adrien Bufort, Stéphane Caron and Abderrahmane Kheddar. SII 2016, Sapporo, Japan, December 2016. Best Paper Award. (pdf)

    In this paper, we challenge the estimation of contact forces backed with ground-truth sensing in human whole-body interaction with the environment, from motion capture only. Our novel method makes it possible to get rid of cumbersome force sensors in monitoring multi-contact motion together with force data. This problem is very challenging. Indeed, while a given force distribution uniquely determines the resulting kinematics, the converse is generally not true in multi-contact. In such scenarios, physics-based optimization alone may only capture force distributions that are physically compatible with a given motion rather than the actual forces being applied. We address this indeterminacy by collecting a large-scale dataset on whole-body motion and contact forces humans apply in multi-contact scenarios. We then train recurrent neural networks on real human force distribution patterns and complement them with a second-order cone program ensuring the physical validity of the predictions. Extensive validation on challenging dynamic and multi-contact scenarios shows that the method we propose can outperform physical force sensing both in terms of accuracy and usability.

P2P storage systems
  • P2P Storage Systems: Study of Different Placement Policies
    Stéphane Caron, Frédéric Giroire, Dorian Mazauric, Julian Monteiro and Stéphane Pérennes. Peer-to-Peer Networking and Applications, Springer, March 2013. (pdf)

    In a P2P storage system using erasure codes, a data block is encoded in many redundancy fragments. These fragments are then sent to distinct peers of the network. In this work, we study the impact of different placement policies of these fragments on the performance of storage systems. Several practical factors (easier control, software reuse, latency) tend to favor data placement strategies that preserve some degree of locality. We compare three policies: two of them are local, in which the data are stored in logical neighbors, and the other one, global, in which the data are spread randomly in the whole system. We focus on the study of the probability to lose a data block and the bandwidth consumption to maintain such redundancy. We use simulations to show that, without resource constraints, the average values are the same no matter which placement policy is used. However, the variations in the use of bandwidth are much more bursty under the local policies. When the bandwidth is limited, these bursty variations induce longer maintenance time and henceforth a higher risk of data loss. We then show that a suitable degree of locality could be introduced in order to combine the efficiency of the global policy with the practical advantages of a local placement. Additionally, we propose a new external reconstruction strategy that greatly improves the performance of local placement strategies. Finally, we give analytical methods to estimate the mean time to the occurrence of data loss for the three policies.

  • Data Life Time for Different Placement Policies in P2P Storage Systems
    Stéphane Caron, Frédéric Giroire, Dorian Mazauric, Julian Monteiro and Stéphane Pérennes. Globe 2010. (pdf)

    Peer-to-peer systems are foreseen as an efficient solution to achieve reliable data storage at low cost. To deal with common P2P problems such as peer failures or churn, such systems encode the user data into redundant fragments and distribute them among peers. The way they distribute it, known as placement policy, has a significant impact on their behavior and reliability. In this paper, we study the impact of different placement policies on the data life time. More precisely, we describe methods to compute and approximate the mean time before the system loses data (Mean Time to Data Loss). We compare this metric for three placement policies: two of them local, in which the data is stored in logical peer neighborhoods, and one of them global in which fragments are parted uniformly at random among the different peers.

  • P2P Storage Systems Data Life Time for Different Placement Policies
    Stéphane Caron, Frédéric Giroire, Dorian Mazauric, Julian Monteiro and Stéphane Pérennes. AlgoTel 2010. (pdf)

    Peer-to-peer systems are foreseen as an efficient solution to achieve reliable data storage at low cost. To deal with common P2P problems such as peer failures or churn, such systems encode the user data into redundant fragments and distribute them among peers. The way they distribute it, known as placement policy, has a significant impact on their behavior and reliability. In this report, after a brief state-of-the-art of the technology used in P2P storage systems, we compare three different placement policies: two of them local, in which the data is stored in logical peer neighborhoods, and on of them global in which fragments are parted at random among the different peers. For each policy, we give either Markov Chain Models to efficiently compute the Mean Time To Data Loss (which is closely related to the probability to lose data) or approximations of this quantity under certain assumptions. We also attempt to give lower bounds on P2P storage systems introducing the BIG system, in which we consider information globally. We propose various ways to compute a bound on the probability to lose data, in relation with parameters such as the peer failure rate of the peer bandwidth.

Signal processing
  • Parametric recurrence quantification analysis of autoregressive processes for pattern recognition in multichannel electroencephalographic data
    Sofiane Ramdani, Anthony Boyer, Stéphane Caron, François Bonnetblanc, Frédéric Bouchara, HuguesDuffau and Annick Lesne. Pattern Recognition. Submitted July 2019. Accepted August 2020. To appear in January 2021.

    Recurrence quantification analysis (RQA) is an acknowledged method for the characterization of experimental time series. We propose a parametric version of RQA, pRQA, allowing a fast processing of spatial arrays of time series, once each is modeled by an autoregressive stochastic process. This method relies on the analytical derivation of asymptotic expressions for five current RQA measures as a function of the model parameters. By avoiding the construction of the recurrence plot of the time series, pRQA is computationally efficient. As a proof of principle, we apply pRQA to pattern recognition in multichannel electroencephalographic (EEG) data from a patient with a brain tumor.

Smart grid energy management
  • Incentive-based Energy Consumption Scheduling Algorithms for the Smart Grid
    Stéphane Caron and George Kesidis. IEEE SmartGridComm 2010. (pdf)

    In this paper, we study Demand Response (DR) problematics for different levels of information sharing in a smart grid. We propose a dynamic pricing scheme incentivizing consumers to achieve an aggregate load profile suitable for utilities, and study how close they can get to an ideal flat profile depending on how much information they share. When customers can share all their load profiles, we provide a distributed algorithm, set up as a cooperative game between consumers, which significantly reduces the total cost and peak-to-average ratio (PAR) of the system. In the absence of full information sharing (for reasons of privacy), when users have only access to the instantaneous total load on the grid, we provide distributed stochastic strategies that successfully exploit this information to improve the overall load profile. Simulation results confirm that these solutions efficiently benefit from information sharing within the grid and reduce both the total cost and PAR.